trw-mcp 0.55.15

Engineering memory MCP server for Claude Code — patterns, gotchas, and project knowledge that persist across sessions

trw-mcp

MCP server for AI coding agents — persistent engineering memory, knowledge compounding, and spec-driven development workflows. Part of TRW Framework.

Every AI coding tool resets to zero. TRW preserves state across sessions via the .trw/ directory — session-start recall replays prior learnings at every new session.

Part of TRW Framework

trw-mcp is the MCP server component of TRW (The Real Work) — a methodology layer for AI-assisted development that turns each coding session's discoveries into permanent institutional knowledge. It works alongside trw-memory, the standalone memory engine.

• trw-mcp (this repo): MCP server with 43 tools, 26 skills, 12 agents
• trw-memory: Standalone memory engine with hybrid retrieval, scoring, and lifecycle

What It Does

trw-mcp is a Model Context Protocol server that gives AI coding agents persistent engineering memory. It records what you learn during development sessions — patterns, gotchas, architecture decisions — and recalls relevant knowledge at the start of every new session. Over time, your AI coding assistant accumulates captured learnings in .trw/ and recalls them at session start. Whether this yields measurable task-completion lift is an open empirical question; early SWE-bench single-shot measurements (n=40/47) showed null. See the verification docs for the current methodology and evidence posture.

The server also manages structured run tracking (phases, checkpoints, events), build verification (pytest + mypy), spec-driven development with AARE-F PRDs, CLAUDE.md auto-generation from high-impact learnings, and instruction-tool manifest validation that ensures agents only see tools they can actually call.

Dogfooding scale: thousands of tests across hundreds of PRDs, dogfooded across the TRW monorepo (coverage gate enforced at 80%, 90% target for new code). This codebase was built by AI agents using TRW. Scale proves the framework is usable at volume; whether it improves outcomes vs baseline is measured via the eval bench, not inferred from these counts.

Quick Start

See the full quickstart guide for Claude Code, Cursor, opencode, and Codex setup.

# Recommended: one-line installer (sets up trw-mcp + bootstraps your project)
curl -fsSL https://trwframework.com/install.sh | bash

# Deploy TRW to a project (must be a git repo)
trw-mcp init-project /path/to/your/repo

# Or add the MCP server to Claude Code manually
claude mcp add trw -- trw-mcp --debug

Manual / advanced install

# Install from PyPI
pip install trw-mcp

# Or install from source
git clone https://github.com/wallter/trw-mcp.git
cd trw-mcp
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"

Deploy to a Project

trw-mcp init-project bootstraps the full TRW framework in any git repository. Full configuration reference at trwframework.com/docs/config.

trw-mcp init-project .              # current directory
trw-mcp init-project /path/to/repo  # specific project
trw-mcp init-project . --ide codex  # force Codex bootstrap
trw-mcp init-project . --force      # overwrite existing files

This creates:

• .trw/ — learning memory, run state, configuration
• .mcp.json — MCP server connection for Claude Code
• CLAUDE.md — project instructions with TRW ceremony protocol
• .claude/hooks/ — ceremony enforcement hooks
• .claude/skills/ — workflow automation skills
• .claude/agents/ — specialized sub-agents

Configuration

Settings via environment variables (prefix TRW_) or .trw/config.yaml. Full reference at trwframework.com/docs/config.

# .trw/config.yaml — top settings (all optional, shown with defaults)
embeddings_enabled: true           # Vector search on by default (install the [vectors] extra to use it)
learning_max_entries: 500          # Max learnings before auto-pruning
build_check_enabled: true          # Run pytest+mypy on trw_build_check
deliver_gate_mode: "block_coding"  # Block delivery for coding/rca/eval tasks without a passing build record;
                                   # set to "advisory" to restore warn-only posture (changed 2026-06-10)
observation_masking: true          # Reduce verbosity in long sessions
ceremony_mode: "full"              # "full" or "light"

Telemetry & network behavior

trw-mcp is local-first: with the default configuration it persists everything under your project's .trw/ directory and makes no outbound network calls except the optional embedding-model download described below. There is no built-in usage tracking, phone-home, or content upload unless you explicitly enable it.

What can touch the network, when, and how to turn it off

Surface	When	Default	Opt-out / control
Embedding model download	First vector operation downloads all-MiniLM-L6-v2 from huggingface.co (only when the [vectors]/[embeddings] extra is installed)	embeddings_enabled: true	TRW_OFFLINE=1 (or HF_HUB_OFFLINE=1) suppresses the download and degrades to keyword-only recall; a disclosure log line is emitted before any fetch
Usage telemetry	Only if explicitly enabled	off (gated by platform_telemetry_enabled, default false)	leave platform_telemetry_enabled=false; see PRD-SEC-004
Learning-content publishing	Only if explicitly enabled	off (gated by learning_sharing_enabled, default false)	leave learning_sharing_enabled=false; learning content is never published off-box by default

With TRW_OFFLINE=1 set, session_start makes zero huggingface.co calls — a testable invariant for air-gapped deployments.

Environment-variable inventory

Variable	Purpose	Default
TRW_OFFLINE	Master offline switch — blocks the huggingface.co embedding-model download	unset (online)
HF_HUB_OFFLINE	Upstream huggingface_hub offline switch — also honored by trw-mcp	unset
TRW_PROBE_ENABLED	Enables the optional sandboxed trw_probe experiment tool	unset (probe disabled)
ENABLE_TOOL_SEARCH	Force-enable/disable MCP tool-search auto-deferral (true/false)	auto-detected
TRW_LOG_LEVEL	Explicit log level (DEBUG/INFO/WARNING/ERROR/CRITICAL)	derived from --debug / defaults
TRW_PLATFORM_API_KEY	Platform credential (PRD-SEC-005) — read from the environment, kept out of git-tracked config	unset
TRW_CONFIG_STRICT	Fail closed on a malformed .trw/config.yaml instead of reverting to defaults	unset (fail-open, but loud)
MEMORY_*	trw-memory engine knobs (see the trw-memory README)	per-field

A malformed .trw/config.yaml always emits a WARNING (and a stderr notice) rather than being silently discarded; set TRW_CONFIG_STRICT=1 to make the load fail closed so security overrides are never dropped unnoticed.

Security defaults

Capability	Default	Notes
Field-level encryption	off	opt-in via trw-memory encryption_enabled
Secret redaction in logs	on	API keys, tokens, and secret-named fields are masked in log output by default
PII detection (memory content)	warn	PII (emails, API keys, etc.) is detected and logged but stored as-is by default (pii_action: warn); set pii_action: block to reject such writes, or redact to mask them
Recall output filtering	redact	SEC-001 recall filter masks flagged values returned by recall (recall_filter_mode: redact)
Memory poisoning detection	observe	detects and records statistical anomalies, does not quarantine, by default
Remote sync / publishing	off	learning_sharing_enabled=false, platform_telemetry_enabled=false
.trw/ directory permissions	0700	state/secret dirs are owner-only
memory.db / secret files	0600	owner read/write only (consistent with pins.json)

Enterprise hardening recipe

For an air-gapped or compliance-sensitive deployment:

export TRW_OFFLINE=1            # no huggingface.co egress; keyword-only recall
export TRW_CONFIG_STRICT=1      # malformed config fails closed, never silently reverts
# Leave telemetry + learning-sharing at their secure defaults:
#   platform_telemetry_enabled: false
#   learning_sharing_enabled:   false

Then verify: .trw/ dirs are 0700, memory.db is 0600, and no outbound connection is attempted at session_start.

MCP Tools (43)

The table below covers the most-used tools out of the full 43. For the complete, always-current list run trw-mcp config-reference or browse the tool reference docs.

Category	Tools	Purpose
Session	session_start, init, status, checkpoint, pre_compact_checkpoint, heartbeat, adopt_run	Run lifecycle, progress tracking, and pin/liveness management
Learning	learn, learn_update, recall, instructions_sync	Knowledge capture, retrieval, and instruction-file refresh
Quality	build_check, review, deliver	Verification and delivery
Requirements	prd_create, prd_validate, prd_diff	Spec-driven development with AARE-F PRDs
Code intelligence	code_search, code_symbol, code_index_update, before_edit_hint, codebase_risk_report, entity_risk_map	Repo-aware search, symbol lookup, and risk signals
Observability	query_events, surface_diff, mcp_security_status	Event history, surface diffs, and security status

Skills (26)

Slash-command workflows — zero tokens until triggered. Full skill reference at trwframework.com/docs.

Sprint & Delivery: /trw-sprint-init · /trw-deliver · /trw-commit

Requirements: /trw-prd-new · /trw-prd-ready · /trw-prd-groom · /trw-prd-review · /trw-exec-plan

Quality: /trw-audit · /trw-self-review · /trw-simplify · /trw-dry-check · /trw-security-check · /trw-test-strategy

Framework: /trw-framework-check · /trw-project-health · /trw-memory-audit · /trw-memory-optimize

Agents (12)

Specialized sub-agents for Agent Teams — parallel execution with coordinated handoffs:

Role	Agent	Purpose
Core Team	trw-lead, trw-implementer, trw-tester, trw-researcher, trw-reviewer, trw-auditor, trw-adversarial-auditor	Orchestration, TDD, testing, research, review, audit, spec-vs-code audit
Requirements	trw-prd-groomer, trw-requirement-writer, trw-requirement-reviewer	PRD lifecycle specialists
Quality	trw-traceability-checker, trw-code-simplifier	Traceability and code health

The 6-Phase Model

TRW implements a structured execution lifecycle: RESEARCH → PLAN → IMPLEMENT → VALIDATE → REVIEW → DELIVER with phase gates, build checks, adversarial audits, and delivery ceremony. See FRAMEWORK.md for the full specification, or read the lifecycle overview at trwframework.com/docs/lifecycle.

CLI Commands

trw-mcp init-project .                # Deploy TRW to a project
trw-mcp update-project .              # Update existing installation
trw-mcp check-instructions .          # Validate instruction-tool parity (exit 1 on mismatch)
trw-mcp audit .                       # Audit TRW configuration
trw-mcp config-reference              # Print all TRW_ environment variables
trw-mcp export --format json          # Export learnings
trw-mcp uninstall .                   # Remove TRW from a project

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest tests/ -v --cov=trw_mcp --cov-report=term-missing

# Type checking (strict mode)
mypy --strict src/trw_mcp/

# Targeted testing during development
pytest tests/test_tools_learning.py -k "test_recall" -v

Architecture

src/trw_mcp/
  server/             # FastMCP entry point, middleware chain
  bootstrap/          # init-project: deploy TRW to target repos
  models/             # Pydantic v2 models (config, run, learning, etc.)
  tools/              # MCP tool implementations
  state/              # State management (persistence, validation, analytics)
  middleware/         # FastMCP middleware (ceremony, observation masking, response optimizer)
  telemetry/          # Telemetry pipeline (models, sender, anonymizer)
  data/               # Bundled hooks, skills, agents for init-project

Troubleshooting

MCP connection error: "[Errno 2] No such file or directory" The MCP server process crashed. In Claude Code, type /mcp to reconnect. For other clients, restart your CLI tool.

trw_session_start() returns "No learnings found" This is normal on first use — learnings accumulate as you work. Call trw_learn() to save discoveries, then trw_deliver() to persist them.

stale .trw/ state after upgrading Run trw-mcp update-project . to migrate your project state to the latest schema. If issues persist, backup and re-initialize with trw-mcp init-project . --force.

Embeddings not working despite embeddings_enabled=true Embeddings require the [vectors] extra: pip install 'trw-mcp[vectors]'. Without it, vector search silently degrades to keyword-only.

Debugging

Enable debug logging:

trw-mcp --debug serve              # Debug mode with file logging
TRW_LOG_LEVEL=DEBUG trw-mcp serve  # Via environment variable

Logs are written to .trw/logs/trw-mcp-YYYY-MM-DD.jsonl.

License

Business Source License 1.1 — source-available, free for non-competing use. Converts to Apache 2.0 on 2030-03-21. See the full license terms.

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Built by Tyler Wall · TRW Framework · Documentation · License