Ephemeral Buffer MCP Server (ephemeral-buffer)

An ephemeral in-memory command output capture and hybrid search engine (BM25 + Semantic Embeddings) for AI coding assistants (Claude Code, Antigravity, Cursor, etc.).

🎯 The Problem This Solves

When coding agents run commands that generate large outputs (thousands of lines of build logs, test runs, stack traces, JSON dumps), agents face two failure modes:

1. Context Pollution: Ingesting megabytes of raw text blows out token limits and degrades model reasoning.
2. Blind Bash Filtering: Agents waste multiple turns running head, tail, grep, and awk trying to guess error patterns.

💡 The Solution

ephemeral-buffer provides a transient in-memory ring buffer with Dual Hybrid Indexing and Content-Aware Structure Parsing:

• BM25 Lexical Search (SQLite FTS5): For exact matches on error codes (NullPointerException, ECONNREFUSED, exit 137, HTTP 502). Builds without SQLite FTS5 use a complete token-based Python fallback with lower ranking performance.
• Dense Semantic Vector Search (FastEmbed ONNX): For fuzzy conceptual queries ("Where did the DB connection pool fail?" or "Why did authentication fail?").
• Unified Diff Structural Mapping: Automatically detects git diffs and PR diffs (gh pr diff, git show, git diff), parses modified files, additions/deletions, and generates a line-indexed file map in the summary.
• Smart Signal Filtering: Scans command/build/test logs for diagnostic keywords, suppresses false positives in diffs and source code, and accurately captures test runner failures, unhandled exceptions, and merge conflicts. Use content_type='log' when a plain-text capture should be signal-scanned.
• Successful test-run summaries such as OK or 25 passed suppress fixture-only error and failure keywords while retaining the original output for search.
• Reciprocal Rank Fusion (RRF): Blends lexical and semantic ranking for high precision retrieval.
• LRU Capture Eviction: Holds up to 25 captures and 50 MiB of captured content by default, evicting the least recently used captures when either limit is reached.
• Thread-Safe Shared Engine: Serializes ingestion, search, LRU updates, eviction, and cleanup across MCP requests and CLI socket clients.

📦 Installation

Requirements

• Python 3.10 or newer
• A supported MCP client if you want to use the server from an AI coding assistant
• Network access on first use if FastEmbed needs to download its embedding model

The package is installed from PyPI as ephemeral-buffer-mcp. It provides both the MCP server and the ephbuf command-line client.

Recommended: install from PyPI

Create an isolated virtual environment and install the latest published package:

python3 -m venv .venv
.venv/bin/python -m pip install --upgrade pip
.venv/bin/python -m pip install ephemeral-buffer-mcp

Verify that the CLI is available:

.venv/bin/ephbuf --help

On Windows, use the equivalent commands from the virtual environment's Scripts directory:

py -3 -m venv .venv
.venv\Scripts\python.exe -m pip install --upgrade pip
.venv\Scripts\python.exe -m pip install ephemeral-buffer-mcp
.venv\Scripts\ephbuf.exe --help

To upgrade or remove the package:

.venv/bin/python -m pip install --upgrade ephemeral-buffer-mcp
.venv/bin/python -m pip uninstall ephemeral-buffer-mcp

Install from a source checkout

Use an editable install when developing or testing local changes:

git clone https://github.com/k-rister/ephemeral-buffer-mcp.git
cd ephemeral-buffer-mcp
python3 -m venv .venv
.venv/bin/python -m pip install --upgrade pip
.venv/bin/python -m pip install -e .

The editable install exposes the same ephbuf command and MCP server as the PyPI installation. The reproducible, locked contributor environment is documented in Testing the Server.

When launching from a source checkout with run.sh, the launcher prefers .venv/bin/python, then supports the legacy venv/bin/python layout, before using an explicit PYTHON override or python3. An invalid PYTHON override fails with an actionable error.

Start the MCP server manually

The MCP server uses stdio for communication with the MCP host. Start it with the Python interpreter from the environment where the package was installed:

.venv/bin/python -m server

Normally you should let your MCP client start this process automatically. Do not start a separate server for every shell command: the ephbuf CLI sends captures to the running server over its local Unix socket.

Configure an MCP client

MCP clients generally need the command, arguments, and environment used to start a stdio server. A portable configuration looks like this:

{
  "mcpServers": {
    "ephemeral-buffer": {
      "command": "/absolute/path/to/ephemeral-buffer-mcp/.venv/bin/python",
      "args": ["-m", "server"]
    }
  }
}

Replace the command with the absolute path to the virtual-environment Python interpreter. Using an absolute path avoids differences between GUI-launched clients and interactive shells. On Windows, use a path such as C:\\path\\to\\ephemeral-buffer-mcp\\.venv\\Scripts\\python.exe.

For clients that provide a command-line registration command, register the same executable and arguments conceptually as:

command: /absolute/path/to/.venv/bin/python
arguments: -m server

After saving the configuration, restart or reload the MCP client and confirm that the ephemeral-buffer tools appear. The server exposes capture, search, summary, slice, consolidation, diagnostics, and cleanup tools; the ephbuf CLI is a separate convenience client for shell output.

Isolate concurrent agent sessions

For a coding agent that may run alongside another agent, configure the same session identity for the MCP server and the ephbuf CLI:

"env": {
  "EPHEMERAL_REQUIRE_ISOLATION": "1",
  "EPHEMERAL_SESSION_ID": "agent-session-1"
}

The server and CLI then derive the same session-specific socket, while a missing identity fails closed instead of falling back to the shared legacy socket. EPHEMERAL_SOCKET_PATH may be used instead when the launcher assigns the socket path directly. The MCP initialization instructions describe this policy to the client, but the environment checks enforce it independently.

The CLI bounds each socket connect, send, and receive operation to 10 seconds by default. Set EPHEMERAL_SOCKET_TIMEOUT_SECONDS to a positive number of seconds when a different limit is appropriate; timeout failures return a nonzero CLI result.

First-use model initialization

FastEmbed loads the embedding model lazily on the first semantic or hybrid search, rather than during capture or when the server process imports. The first semantic operation may therefore take longer and may download model files. Subsequent operations use the local model cache. To select a compatible model or move the cache, set:

export EPHEMERAL_EMBEDDING_MODEL="BAAI/bge-small-en-v1.5"
export EPHEMERAL_FASTEMBED_CACHE_DIR="$HOME/.cache/ephemeral-buffer"

Semantic indexing can optionally be prefetched after ingestion:

export EPHEMERAL_SEMANTIC_PREFETCH=1
export EPHEMERAL_SEMANTIC_PREFETCH_WORKERS=1

Prefetch is disabled by default. When enabled, the bounded worker pool indexes captures in the background while ingestion returns; semantic and hybrid search wait for a relevant in-progress job, and failed jobs retry through the normal lazy path. Evicted or cleared captures do not retain queued work. Keep the worker count small because embedding generation competes for host CPU and memory; get_buffer_stats and get_runtime_diagnostics expose only aggregate pending/failed counts.

The exact model and cache location can also be supplied in the MCP client's env configuration. Keep the model cache writable by the user running the MCP client.

Installation choices at a glance

If the MCP client cannot find the server, check the absolute interpreter path, the selected Python environment, and the client's server logs. For socket, capture-limit, logging, and deployment troubleshooting, see OPERATIONS.md.

🏗 Architecture & Flow

flowchart TD
    subgraph Ingestion["1. Ingestion Paths"]
        A["CLI Pipe: command 2>&1 | ephbuf"] --> D["Unix Socket (platform temp dir)"]
        B["Agent Tool: execute_and_capture(cmd)"] --> E["Ephemeral Ring Buffer Engine"]
        C["Agent Tool: capture_text / capture_file"] --> E
        D --> E
    end

    subgraph Indexing["2. Dual Hybrid Indexing & Classification"]
        E --> F["SQLite FTS5 (BM25 Lexical) or Python lexical fallback"]
        E --> G["FastEmbed ONNX (Dense Vectors)"]
        E --> K["Diff & Signal Parser (File Maps & Conflict Detection)"]
    end

    subgraph Querying["3. Agent Query & Retrieval"]
        F & G --> H["Reciprocal Rank Fusion (RRF)"]
        H --> I["search_capture(query, mode='hybrid')"]
        K --> L["Diff File Map & get_capture_slice"]
        I --> J["Precise Context Chunk + Line Numbers"]
    end

🚀 How to Use It

1. From the Terminal (CLI Pipe via ephbuf)

You can pipe command output directly into the running MCP server:

# Pipe any command output into the buffer
pytest -v 2>&1 | ephbuf --label "pytest run"

# Pipe git diffs directly
git diff HEAD~3 | ephbuf --label "feature diff" --type diff

# Or wrap command execution
ephbuf --label "backend build" -- cargo build --verbose

The optional --type/-t hint accepts auto (the default), diff, log, or text. Use diff for unified patches when automatic detection is ambiguous; otherwise auto classifies diffs, build/test logs, and plain text from the content and label.

ephbuf also bounds wrapped-command and piped-stdin capture with --max-output-bytes; it defaults to EPHEMERAL_MAX_BUFFER_BYTES or 50 MiB and retains the beginning and end of oversized output. Use --timeout-seconds to stop a wrapped command after a bounded runtime; timed out commands retain the output collected so far and exit with status 124. Requested max_output_bytes and capture_file max_bytes values may not exceed the configured buffer byte limit; the tools return a validation error instead of silently clamping them.

2. From the AI Agent via MCP Tools

The agent has access to the following tools:

Choose the execution path based on the output and inspection goal:

• Use direct command execution for a small, targeted inspection where the output is already bounded and immediate terminal feedback is sufficient.
• Use execute_and_capture once output may be noisy, large, or uncertain—such as tests, builds, and logs—because it bounds context and makes later search and exact retrieval available. This is an advisory heuristic, not a hard line-count policy.
• Use capture_text when output is already in hand, or capture_file for a file that has been checked and intentionally selected for ingestion.

Use preflight_command when repository identity or path resolution is uncertain before a sensitive command. It reports resolved facts and explicit unavailable states without running the requested command or exposing command output. It cannot predict shell expansion, aliases, pipelines, redirections, environment changes, or arbitrary shell logic, so normal command validation and user intent checks remain necessary.

Before any repository-sensitive command or file capture, verify the intended working directory and target path. Prefer an explicit cwd, confirm the repository identity, and resolve symlinks when path identity matters. Shell expansion, inherited working directories, and symlinks can target a different location than the spelling suggests. Capture limits protect context size; they do not validate command intent, path identity, or filesystem safety.

For diff captures, get_capture_summary reports the detected file map, addition/deletion statistics, line ranges, and merge-conflict signals. Use get_capture_slice with those ranges to retrieve the complete file context.

Consolidating multi-result workflows

When a workflow produces several captures—for example, one command per repository—use consolidate_captures to give the agent one bounded overview:

consolidate_captures(
  capture_ids=["cap_1", "cap_2", "cap_3"],
  label="organization activity",
  max_captures=25,
  max_bytes=20000
)

The resulting capture is JSON with source metadata and records containing the original capture_id and source_line. It can be searched normally with search_capture, and exact consolidated context can be retrieved with get_capture_slice. The response reports omitted records, missing IDs, and the original source IDs; use those original IDs to retrieve complete detail when the consolidated byte budget is reached. Calling the tool without capture_ids consolidates the currently active captures, up to max_captures.

This workflow keeps the server responsible for bounded execution, storage, and retrieval while leaving prioritization and interpretation to the coding agent.

3. Capture Hygiene

Keep captures focused so search results remain useful and the agent receives only the context it needs:

• Capture one command or related output stream at a time, using a descriptive label.
• Start with get_capture_summary, then use search_capture or get_capture_slice for targeted retrieval instead of repeatedly recapturing the same output.
• Use clear_captures(capture_id) when a capture is no longer needed; use clear_captures("all") between unrelated investigations.

The buffer is intentionally transient and bounded by the LRU capture limit, but explicit cleanup prevents recent investigations from obscuring the active one before automatic eviction occurs. Its memory metrics separate captured content and embedding bytes from process RSS; the unaccounted RSS value includes model, index, and Python object overhead and is approximate.

The server defaults can be overridden with EPHEMERAL_MAX_CAPTURES and EPHEMERAL_MAX_BUFFER_BYTES. Session-aware launchers can set EPHEMERAL_SESSION_ID so each server/CLI pair automatically derives a unique socket path; EPHEMERAL_SOCKET_PATH remains an explicit override. The byte limit accounts for captured UTF-8 content plus its label; a capture is rejected when their combined size exceeds the limit. get_buffer_stats also reports embedding model readiness, embedding/cache settings, and process memory separately.

Indexed chunks are bounded separately by EPHEMERAL_MAX_INDEXED_CHUNKS, which defaults to 32,768 total chunks across retained captures. LRU eviction makes room for a new capture when possible. A capture that exceeds the entire index budget is rejected rather than partially indexed, so accepted captures remain fully searchable and cannot produce silent semantic false negatives. execute_and_capture retains the beginning and end of oversized command output and marks the capture with its original byte count. Each returned head or tail preview is independently capped at 4 KiB of UTF-8 data; a truncation marker directs the agent to get_capture_slice for the complete content.

Call get_runtime_diagnostics() when reporting a field observation. It is explicitly opt-in and returns operational metadata only; captured content, labels, command arguments, and session ID values are excluded. Sanitize any additional output before sharing it.

Operational events are written as privacy-safe JSON lines to stderr. Warnings and errors are enabled by default; set EPHEMERAL_LOG_LEVEL=INFO to include normal readiness, eviction, process lifecycle, and MCP tool start/completion events. Tool lifecycle events contain only a local call ID, tool name, duration, success state, and error class. Logs never include captured content, labels, query text, or command text. A start event without a matching completion or failure event identifies a stalled tool/session boundary. The Codex A/B adapter can persist these events per MCP run with --diagnostic-log-dir /path/to/logs; keep that directory outside the repository. The files contain lifecycle metadata only.

Optional local usage metrics

Set EPHEMERAL_METRICS=1 to collect content-free, in-process usage metrics. The metrics include per-tool call counts, success/failure counts, duration totals, and aggregate capture/search/retrieval, empty-search, eviction, and cleanup events. They are disabled by default, are never sent anywhere, and do not retain captured content, labels, commands, or query text. When enabled, both get_runtime_diagnostics() and get_buffer_stats() include the same aggregate metrics snapshot. The event keys are stable and zero-filled when no event has occurred. Metrics are process-lifetime state: restarting the server clears them, while capture-associated correlation state is released when a capture is evicted or explicitly cleared.

Effectiveness metrics and privacy

The built-in metrics are local, opt-in operational telemetry. Set EPHEMERAL_METRICS=1 only when you want measurements for the current server process; nothing is uploaded or shared by the server. The metrics contain counts, durations, byte sizes, and bounded lifecycle outcomes, but do not retain captured content, labels, command arguments, or query text. Runtime logs follow the same privacy model. Treat any captured output or diagnostic excerpt as potentially sensitive and sanitize it before sharing.

Interpret the measurements in two separate layers:

The effectiveness harness measures server-side behavior with deterministic fixtures and does not invoke a coding-agent model. A successful targeted retrieval means only that the fixture's expected marker was found. It is not a measure of answer quality, search relevance in a real repository, token cost, or end-to-end task completion. Use representative, privacy-reviewed tasks for those questions and report the fixture, seed, repetition count, success rate, useful-search rate, byte measurements, and local timing separately.

For a reproducible local diagnostic, start the server with EPHEMERAL_METRICS=1, exercise the workflow, then request get_runtime_diagnostics() and get_buffer_stats(). A safe bug report includes the version/commit, Python/platform, configuration limits, operation name, reproduction steps, and sanitized metric output; it excludes captures, credentials, tokens, private paths, source code, user data, and raw queries.

See OPERATIONS.md for deployment settings, troubleshooting, release verification, and repository maintenance procedures.

🛠 Testing the Server

Set up a local development environment from a fresh checkout:

python3.12 -m venv .venv
.venv/bin/python -m pip install --require-hashes -r requirements-dev-lock-py312.txt

The committed requirements-dev-lock-py312.txt file is the reproducible Python 3.12 development and release environment. Python 3.10 remains supported through the direct requirements and tested constraints.txt file; the CI matrix exercises both paths. Keep requirements.txt and requirements-dev.txt as the reviewable dependency inputs, and regenerate the Python 3.12 locks with pip-tools after an intentional dependency update:

.venv/bin/python -m pip install pip-tools
.venv/bin/pip-compile --generate-hashes --output-file=requirements-lock-py312.txt requirements.txt
.venv/bin/pip-compile --generate-hashes --output-file=requirements-dev-lock-py312.txt requirements-dev.txt

Review the resulting changes, run the full test matrix, and run pip-audit before merging. Downstream users install the package normally; its compatible dependency ranges in pyproject.toml are intentionally not replaced by the development locks.

Run the test suite:

.venv/bin/python -m unittest test_engine.py test_capture_utils.py test_config.py test_cli.py test_server.py
.venv/bin/python -m unittest test_e2e_pipe.py

Measure focused-test coverage locally:

.venv/bin/python -m coverage run --source=. --omit='test_*.py,benchmark_concurrency.py,benchmark_effectiveness.py,benchmark_latency.py,benchmark_agent_ab_repository_fixture.py,release_checks.py' -m unittest test_benchmark_concurrency.py test_benchmark_effectiveness.py test_release_checks.py test_benchmark_agent_ab_repository_fixture.py test_engine.py test_capture_utils.py test_config.py test_cli.py test_server.py
.venv/bin/python -m coverage report

The current focused-test baseline is 91%; CI enforces a 91% minimum after adding coverage for defensive command, limit, cleanup, embedding, and socket handling paths. Coverage reports are uploaded for inspection, and future threshold increases should follow similarly targeted test additions. The release guardrail utility is measured separately because it is a workflow utility rather than application runtime code:

COVERAGE_FILE=.coverage.release .venv/bin/python -m coverage run --source=. -m unittest test_release_checks.py
COVERAGE_FILE=.coverage.release .venv/bin/python -m coverage report --include='release_checks.py'

GitHub Actions runs the compile check, focused tests, and end-to-end test on Python 3.10 and 3.12 for pushes to main and pull requests. The FastEmbed model is loaded on the first capture or semantic search rather than during server import. Set EPHEMERAL_EMBEDDING_MODEL to select a compatible model and EPHEMERAL_FASTEMBED_CACHE_DIR to control its cache directory. The model cache is retained between CI runs to reduce startup time. CI unit and end-to-end tests set the internal EPHEMERAL_TEST_EMBEDDINGS=1 flag, which uses a small deterministic embedding substitute so test execution does not depend on a model download; release and benchmark jobs continue to exercise FastEmbed. It also builds the wheel and verifies the installed ephbuf entry point. CI audits the declared dependencies with pip-audit and fails if known vulnerabilities are found. CI installs the hashed Python 3.12 development/runtime locks and uses the tested constraints.txt path for Python 3.10. The direct requirements and constraints are updated only after the full test matrix passes; lock updates must be reviewed together with their resolver output and audit results.

Pushing a version tag such as v0.1.1 runs the release workflow, which first verifies that the tag is valid SemVer, points to a commit contained in the default branch, and starts from a clean checkout. It also requires the tag, pyproject.toml, and a dated matching CHANGELOG.md section to agree. The workflow then builds wheel and source distributions, validates their metadata, verifies the installed package, and uploads the artifacts for review. A failed guardrail reports the mismatched value or source-state problem before building. The workflow creates a GitHub Release using the matching changelog section, attaches the wheel, source distribution, and SHA256SUMS, and links back to the workflow run containing the build-provenance attestation. Verify a downloaded artifact with sha256sum --check SHA256SUMS from the directory containing the files. The same verified distributions are then published to PyPI through trusted publishing. After the repository's pypi environment is configured with a PyPI trusted publisher, the workflow publishes the distributions to PyPI automatically.

Run the concurrency benchmark:

.venv/bin/python benchmark_concurrency.py --captures 32 --workers 8

The benchmark accepts --min-ingest-per-second and --min-reads-per-second thresholds for direct checks. For repeatable regression checks, pass --baseline benchmark_baseline.json --output benchmark-concurrency.json. The checked-in baseline uses a 20% tolerance: a run fails only when ingest or read throughput drops below 80% of its baseline. Each scheduled or manually dispatched GitHub Actions run records the raw JSON result as an artifact and adds the measurements and regression status to the workflow summary. This benchmark remains optional and is not part of the required pull-request checks; update the baseline deliberately when the runner or benchmark workload changes.

Measure command-capture latency by output size and pipeline phase:

EPHEMERAL_TEST_EMBEDDINGS=1 .venv/bin/python benchmark_latency.py \
  --samples 5 --output benchmark-latency.json

The latency harness reports cold-start time plus warm median and p95 timings for command execution, BM25 capture/indexing, deferred semantic indexing, summary generation, and the combined pipeline. Semantic embeddings are now materialized when semantic or hybrid search first needs them, so the warmup explicitly materializes the warm engine's embeddings before timed samples begin. Use the separate semantic-index phase when evaluating end-to-end costs; cold start includes the first engine's model and embedding setup, while warm samples reuse the configured model cache and engine. The benchmark is diagnostic and optional, not a required pull-request check.

Compare lazy semantic indexing with opt-in asynchronous prefetch:

EPHEMERAL_TEST_EMBEDDINGS=1 .venv/bin/python benchmark_prefetch.py \
  --line-count 256 --samples 5 --output benchmark-prefetch.json

The prefetch harness reports ingestion, first semantic search, and subsequent semantic search medians for both modes. Prefetch should reduce first-query latency when work completes during ingestion, while adding bounded background resource use; treat the output as deployment-specific diagnostic evidence.

Measure direct-versus-captured routing tradeoffs with synthetic output profiles:

EPHEMERAL_TEST_EMBEDDINGS=1 .venv/bin/python benchmark_routing.py \
  --samples 5 --output benchmark-routing.json

The routing harness compares direct command completion with bounded capture and summary generation for targeted (16 lines), test-like (256 lines), and build/log-like (2048 lines) output. Use the medians and p95 values to keep the heuristic honest: direct execution generally has lower latency for small, bounded output, while capture adds searchable context and bounded response size for noisy or uncertain output. These synthetic measurements are guidance, not universal thresholds or a required CI gate. Both benchmark summaries use the nearest-rank p95 convention: for n samples, p95 is the value at sorted rank ceil(0.95 * n), with ranks starting at one.

Measure command-output handling effectiveness with deterministic synthetic data:

EPHEMERAL_TEST_EMBEDDINGS=1 .venv/bin/python benchmark_effectiveness.py \
  --mode both --output benchmark-effectiveness.json

The effectiveness harness compares a full-output baseline with an engine-backed MCP workflow across large output, failure logs, diffs, and follow-up searches. It reports per-scenario success, search usefulness, retrievals, bytes examined, resolution time, and an aggregate comparison as machine-readable JSON. Token usage is explicitly marked unavailable because this harness does not invoke a model. Fixtures contain no project content and are generated in code, so runs are reproducible. This is a server-side smoke evaluation, not a claim about any particular coding agent or model.

Evaluate search relevance across supported modes with deterministic synthetic fixtures:

EPHEMERAL_TEST_EMBEDDINGS=1 .venv/bin/python benchmark_relevance.py \
  --top-k 3 --baseline benchmark_relevance_baseline.json \
  --output search-relevance.json

The relevance benchmark covers exact errors, punctuation-heavy queries, conceptual semantic queries, and lexical/semantic conflicts. It reports hit@1, hit@k, and mean reciprocal rank (MRR) for BM25, semantic, and hybrid search. The fixtures define expected retrieval markers, so this measures retrieval relevance only—not agent answer quality, token usage, or universal performance. CI compares the deterministic scores with the checked-in benchmark_relevance_baseline.json and fails only when a metric drops by more than the recorded tolerance. Baseline changes must be deliberate, reviewable, and accompanied by a fixture or intended-behavior explanation. The evaluation uploads its machine-readable JSON result with the other benchmark artifacts.

The baseline stores only schema and fixture versions, deterministic embedding mode, aggregate per-mode scores, query counts, and explicit tolerances. It does not store captures, raw command output, or user queries. Environment-specific latency and effectiveness results remain CI artifacts and summaries rather than exact cross-run gates; retention follows the workflow artifact policy.

Example benchmark results

The reproducible paired evaluation was run on 2026-09-11 with five repetitions, seed 20260907, and deterministic test embeddings. Across four synthetic scenarios, both the direct-output baseline and the MCP workflow completed all 20 tasks, and every MCP search was useful. The MCP workflow examined 30–85% fewer bytes than the baseline per scenario (68% on average):

The consolidation evaluation, using the same seed and five repetitions, reduced the initial multi-result overview from an average of 4,298 bytes to 213 bytes (95% fewer overview bytes), while preserving a 100% targeted retrieval success rate. The consolidated workflow retrieved 3.6% fewer bytes overall and took 2.2 times as long locally as sequential processing in this run. These measurements quantify the MCP data path: fewer bytes need to be returned to the agent before it asks for targeted detail.

They are not universal performance guarantees. The fixtures are synthetic, the harness does not invoke a model, and the byte reduction is not a direct token-savings measurement. In this run, consolidated processing took about 2.2 times longer locally than sequential processing, while retrieving similar detail. The benchmark therefore demonstrates context-size and workflow-shaping benefits, not that every workload will be faster or that search results will be relevant for arbitrary repositories. Re-run the commands below with representative, privacy-reviewed tasks before making project-specific claims.

Run a controlled local A/B evaluation with repeated paired measurements:

EPHEMERAL_TEST_EMBEDDINGS=1 .venv/bin/python benchmark_effectiveness.py \
  --ab-runs 5 --seed 20260907 --output benchmark-effectiveness-ab.json

The A/B report uses the same deterministic fixtures in both modes, seeded task and mode ordering, and local-only measurements. It reports completion rate, mean/min/max time, standard deviation, repeated commands, search usefulness, byte reduction, and local MCP processing overhead for each scenario. The timing ratio covers only local capture, indexing, search, and retrieval; it is not an agent-level performance measurement. Since no model is invoked, token usage is unavailable. Treat the recommendations as synthetic benchmark guidance and repeat the evaluation with representative agent tasks before generalizing the results.

For an end-to-end coding-agent evaluation, generate a counterbalanced, privacy-safe schedule:

.venv/bin/python benchmark_agent_ab.py \
  --schedule-output agent-ab-schedule.json --repetitions 5 --seed 20260909

Run each scheduled task with MCP disabled (control) and enabled (mcp) using the same model configuration, repository fixture, environment, and reset policy. Have an external agent adapter write a records envelope containing only the schedule, non-secret protocol identifiers, and per-run fields: completion, signal retrieval, duration, tool calls, repeated commands, context proxy bytes (total plus prompt/output components), provider usage samples, and peak RSS bytes sampled from that invocation's process. On hosts without a supported per-process RSS interface, peak RSS is recorded as zero and should be treated as unavailable. Summarize it with:

.venv/bin/python benchmark_agent_ab.py \
  --records agent-ab-records.json --output agent-ab-summary.json

The analyzer validates balanced paired runs, reports per-mode aggregates and MCP-minus-control deltas with uncertainty, and emits recommendations. It does not invoke a model, require credentials, or accept raw prompts, transcripts, commands, captures, or user content. Do not commit agent records or generated captures; share only the aggregate summary after privacy review.

The repository includes a Codex CLI adapter for executing that protocol. It uses the requested model explicitly, creates a fresh fixture copy for every run, isolates control and MCP configuration, and writes metadata-only records. Create a private task manifest (do not commit it) with one prompt and optional signal marker for each scheduled task:

{
  "tasks": {
    "targeted-inspection": {"prompt": "Inspect the fixture and report the marker.", "signal_marker": "MARKER"},
    "noisy-test-failure": {"prompt": "Run the fixture test and report the failure marker.", "signal_marker": "MARKER"},
    "build-log-search": {"prompt": "Inspect the build log and report the marker.", "signal_marker": "MARKER"},
    "follow-up-context": {"prompt": "Find the earlier marker and report it.", "signal_marker": "MARKER"}
  }
}

Run the adapter from the repository checkout:

.venv/bin/python run_codex_agent_ab.py \
  --schedule agent-ab-schedule.json \
  --tasks /path/to/private-agent-tasks.json \
  --repository /path/to/privacy-reviewed-fixture \
  --model gpt-5.6-luna \
  --output /tmp/agent-ab-records.json
.venv/bin/python benchmark_agent_ab.py \
  --records /tmp/agent-ab-records.json \
  --output /tmp/agent-ab-summary.json

The runner requires a locally authenticated codex CLI. It uses codex exec --json --ephemeral, uses a read-only sandbox by default. context_bytes_proxy is an observable prompt/event-envelope proxy because the CLI does not expose the model's internal context size. When the fixture does not contain an importable server module, pass --mcp-server-script /absolute/path/to/server.py. For MCP experiments where the client must be allowed to call the configured server, add --allow-mcp-approvals. This uses Codex automatic review with a workspace-write sandbox, and should only be used with a disposable, privacy-reviewed fixture. Control runs continue to use the read-only sandbox without MCP approval routing; the records protocol identifies the selected policy. Add --require-mcp-calls when the MCP arm must exercise at least one MCP tool; runs that bypass MCP are then marked incomplete with reason mcp_not_used. Review prompts, fixtures, and generated records for privacy before sharing; the runner does not persist transcripts in its records output.

Runner records use version 4 and add exit code, failure reason, MCP-specific tool-call count, provider-reported input/output token counts, and every provider usage sample when Codex emits them. They also break the observable context proxy into prompt and output byte components. Version-1 through version-3 records remain readable; missing provider metrics are reported as unavailable rather than zero. Summaries also report usage sample counts, monotonicity observations, and first-to-last deltas. Monotonic samples are explicitly inconclusive: they may be cumulative or per-turn values and require a controlled calibration matrix.

Generate the reviewed synthetic EB-heavy fixture and its task manifest with:

.venv/bin/python benchmark_agent_ab_fixtures.py \
  --fixture-output /tmp/agent-ab-fixture \
  --manifest-output /tmp/agent-ab-tasks.json

The fixture generates large test and build output at runtime, plus a small targeted-output task and a follow-up retrieval task. The manifest includes output-size bands, expected signals, and objective success criteria. It is synthetic and contains no user logs or captured output.

For a repository-shaped evaluation, set AGENT_AB_FIXTURE_PROFILE:

AGENT_AB_FIXTURE_PROFILE=repository-shaped-v1 \
  CODEX_HOME=/path/to/writable/authenticated-codex-home \
  ./run_agent_ab_experiment.sh

This profile contains source, tests, configuration, and repository workflow tooling. Its test and build commands inspect that layout while producing deterministic noisy signals. It is still synthetic and privacy-safe; it does not contain a production repository or user data.

Repeat the complete five-repetition Codex A/B run with the repository script. Set CODEX_HOME to a writable, authenticated Codex home; generated fixtures, records, and lifecycle logs remain under /tmp by default:

CODEX_HOME=/path/to/writable/authenticated-codex-home \
  ./run_agent_ab_experiment.sh

Override AGENT_AB_RUN_DIR, AGENT_AB_MODEL, AGENT_AB_REPETITIONS, AGENT_AB_SEED, or AGENT_AB_TIMEOUT_SECONDS to repeat a different experiment. The script uses the synthetic fixture by default; set AGENT_AB_FIXTURE_PROFILE=repository-shaped-v1 for the repository-shaped synthetic profile. Use the lower-level runner commands above for a privacy-reviewed production-repository fixture and private task manifest.

Create and compare an aggregate agent A/B baseline after a privacy review:

.venv/bin/python benchmark_agent_ab_baseline.py \
  --summary /tmp/agent-ab-summary.json \
  --create-baseline \
  --output benchmark_agent_ab_baseline.json
.venv/bin/python benchmark_agent_ab_baseline.py \
  --summary /tmp/agent-ab-summary.json \
  --baseline benchmark_agent_ab_baseline.json \
  --fail-on-regression \
  --output /tmp/agent-ab-comparison.json

The baseline stores model, fixture, seed, repetition, aggregate metrics, and explicit tolerances only. It never stores prompts, transcripts, commands, captures, or user content. This comparison is a documented manual workflow; live model calls are not part of required pull-request CI. Update a checked-in baseline only when fixture or model changes are explained in review.

The current agent-level evaluation supports a provisional routing policy: prefer MCP for large noisy output and follow-up retrieval, and prefer direct execution for small targeted inspections. Do not treat this as a universal default or convert it into hard numeric thresholds yet. The repository-shaped profile is synthetic, so production-repository behavior should be validated separately before generalizing the result. Provider-reported token deltas are diagnostic until a calibration matrix establishes whether usage samples are cumulative or per-turn.

Compare sequential per-capture retrieval with the consolidated workflow:

EPHEMERAL_TEST_EMBEDDINGS=1 .venv/bin/python benchmark_effectiveness.py \
  --consolidation-runs 5 --seed 20260907 \
  --output benchmark-effectiveness-consolidation.json

This report measures overview and retrieval response bytes, targeted retrieval success, search/retrieval counts, local processing time, and omitted records. It models each synthetic scenario as a repository result and does not invoke a coding-agent model; response-byte reductions therefore describe the MCP data path, not end-to-end agent performance.

When sharing a result, include the command, seed, repetitions, benchmark evaluation name, success rate, useful-search rate, byte reduction, and timing scope. Do not attach generated captures or paste raw command output. Check any surrounding report or wrapper for repository-specific content before sharing the benchmark JSON.