ratemyagent

Test MCP servers like production services.

RateMyAgent is a reliability scanner for MCP servers, and for the agents that call them. Most evaluation asks whether a tool can do the task. This asks whether it stays reliable when operated like a production service — under load, slow dependencies, rate limits, server errors, malformed replies and dropped connections. Pointed at an agent, it asks the question those faults raise for a write: when the reply goes missing, does the agent apply the work twice?

Experimental: the agent path is experimental in its API, not in its evidence. It has been run against Claude Code, the OpenAI Agents SDK and LangGraph (Agents). An agent scan earns a verdict only when it reads the upstream's own state around every task (--verify-tool or --verify-command) and at least one task had a call whose reply never came; otherwise it says NO VERDICT and why. The walkthrough gets you there. The LLM adapter for Anthropic and OpenAI chat completions has never been run against a live API (details).

NOTE: Read-only tools, STAGING rather than production: there's no dry-run yet. Expanding capabilities soon.

Point it at a target and it:

1. Measures a baseline — latency distribution, token cost, where concurrency saturates, and whether the tools enforce their own JSON Schema.
2. Injects faults — timeouts, 429s, 500s, malformed replies and refused connections, through a proxy the target cannot see.
3. Studies what happened — did each disrupted operation recover, and how many calls did it cost.

Then it scores the result 0–100 against a YAML policy you control, gates CI with an exit code, and writes an AGENTS.md fix guide you can hand straight to a coding agent.

It is built for the developer who wrote an MCP server — often with AI help — and wants to know whether it is ready before something depends on it. It tests behaviour, not source: a schema that is declared but never enforced, missing backpressure, or a retry loop that amplifies failures all show up in what the target does.

When the evidence cannot support a number, it says so instead of printing one. An unmeasurable check comes back n/a with the reason attached and leaves the score, rather than counting as a zero or a pass. Why that matters.

What it measures, and what it refuses to score

Five dimensions, weighted to 100. Each can decline.

The recovery threshold is derived, not fixed. The fault injector produces 1 - fault_rate ** retries against a target that never fails — 96% at the default rate — so a fixed floor would grade the flag rather than the target. Every report header states the rate and the floor it implies, because two scans at different rates are not comparable.

Targets: MCP over stdio, Streamable HTTP and SSE, plus five built-in mock profiles that need no server. The LLM adapter is experimental, as above. Outputs: terminal scorecard, markdown report, AGENTS.md, JSON. Every scan reproduces under --seed. Per-probe detail is in docs/PROBES.md.

Install

Published on PyPI. Python 3.10+.

pip install ratemyagent

That gives you the engine, the mock targets, and every output format — enough to run a full scan without installing anything else. The adapters that talk to real systems need their SDKs, which are optional so you only pull what you use:

pip install 'ratemyagent[mcp]'          # scan MCP servers (mcp SDK 1.x or 2.x)
pip install 'ratemyagent[anthropic]'    # scan Anthropic chat completions
pip install 'ratemyagent[openai]'       # scan OpenAI chat completions
pip install 'ratemyagent[all]'          # all of the above

With uv: uv tool install ratemyagent for a standalone CLI, or uv pip install 'ratemyagent[all]' into the current environment.

Your agent, your database, 15 minutes

You have an agent built on the OpenAI Agents SDK (or LangGraph), and an MCP server server.py whose create_order tool writes a row into orders in app.db. This gets you a verdict on what the agent does when a write's reply goes missing: does it apply the write twice? The step times are the budget, not measurements.

1. Install (1 min). Into the Python your agent runs under:

pip install ratemyagent

2. Get the runner and put your agent in it (5 min). Your SDK has no command line, so the runner is the part a scan needs: it reads the scan's MCP config, starts the proxy exactly as written, runs one task and prints one claim. Edit build_agent() to return your agent. The runner attaches the MCP server itself. Change nothing else.

curl -fsSLO https://raw.githubusercontent.com/SMWundefined/RateMyAgent/v1.9.0/examples/runners/run_openai_agents.py
# LangGraph: .../examples/runners/run_langgraph.py

Without GitHub, the same file ships in the source distribution:

pip download ratemyagent --no-binary :all: --no-deps -d rma-sdist
tar -xzf rma-sdist/ratemyagent-*.tar.gz -C rma-sdist
cp rma-sdist/ratemyagent-*/examples/runners/run_openai_agents.py .

3. One task (2 min). Save it as tasks.json. Every field is required, and tool is the tool your agent will call. arguments are read only by --scripted (step 5), so {} is fine for a model.

{"tasks": [{"id": "t1", "prompt": "Create an order for customer 42, item 'rma-probe-1', quantity 1.",
            "expected_effects": 1, "tool": "create_order", "arguments": {}}]}

4. Check the verify command at your own prompt (1 min). It must print a number or a JSON list:

sqlite3 app.db 'select count(*) from orders'

5. Scan (3 min). The first run will usually end in NO VERDICT naming a --seed; that is expected, and step 6 is the re-run.

ratemyagent scan --target agent --agent-kind llm \
    --agent "python run_openai_agents.py" \
    --agent-command '--config {config} --prompt {prompt} --tasks {tasks} --task {task_id}' \
    --tasks tasks.json --upstream "stdio://python server.py --db app.db" \
    --verify-command "sqlite3 app.db 'select count(*) from orders'" \
    --key-path idempotency_key \
    --allow-mutating --lost-reply-close-after --timeout 180 --work-dir ./rma-work

To check the plumbing first at $0, put --scripted after the runner's name in --agent. The runner then calls the task's tool with its arguments in place of a model, so fill those in first.

6. On NO VERDICT: no task had a call whose outcome was unknown (3 min), re-run with the --seed that line names, a new --work-dir (say ./rma-work-2), and a new payload: change rma-probe-1 to rma-probe-2 in tasks.json. The payload changes because app.db keeps step 5's rows. A server that honours idempotency keys absorbs a key the agent reuses from the first scan, and an agent that builds its key from the prompt sends the same key for the same prompt, so the re-run's clean pass would apply nothing and be refused. The seed still applies: it places the fault by task, tool and call number, not by content. The first run's records stay as its evidence, and a scan refuses a directory that already holds some. The budget has room for one re-run.

A verdict is PASS or FAIL on behaviour, with duplicate mutations read from app.db and a retry keys (reached the proxy) line. PASS, UNRECONCILED is a pass whose agent reported failure on work that landed, or success that no reply confirmed. It names the readings and the tasks. If there is no verdict, NO VERDICT gives a reason you can act on:

A single run is one draw; use --repeats 5 for anything you share.

30 seconds, no API key

There is a built-in mock target, so you can see the whole thing work before pointing it at anything real. No key, no server, no network.

ratemyagent scan --target mock --profile degraded --requests 40 \
    --concurrency 16 --fault-rate 0.3

RateMyAgent Scan Results
========================

Target: degraded-mock (mock)
Probes: 6/6 complete   Duration: 0.01s
Faults: fault rate 30%, 2 retries -> recovery floor 91.0% (derived, not the policy value)

Phase 1  baseline
  Latency ................ p50 3.36s, p95 7.99s, p99 - over 40 requests (0.0% errors)
  Cost ................... 647 in / 120 out tokens per request, no price known for this model
  Concurrency ............ no saturation up to 16 concurrent, sustained 16
  Contract ............... 15 edge cases across 3 tools: 0 rejected cleanly, 15 accepted, 0 crashed

Phase 2  chaos (fault injection)
  Fault tolerance ........ 20 faults injected, 10/10 operations recovered (100%) within 2 retries, 1.30x call amplification

Phase 3  behavior analysis
  Behavior ............... 10/10 disrupted operations recovered (100%) within 2 retries, 1.30x amplification (ours), 0 duplicate deliveries (ours)

                             actual     target     status
  p95 latency                7.99s      5.00s      FAIL
  schema violations accepted 9          0          FAIL
  error rate                 0.0%       5.0%       pass ~
  contract crash rate        0.0%       0.0%       pass
  recovery rate              100.0%     91.0%      pass ~
  p99 latency                -          10.00s     n/a ~
  cost per request           -          $0.1000    n/a ~
  retry amplification        -          2.00x      n/a ~
  duplicate mutations        -          0          n/a ~

  ~ recovery rate -- 10/10 disrupted operations recovered, a
    95% interval of 72.2%-100.0%, which spans the 91.0% this
    fault rate produces against a target that never fails.
    recovery_rate_min is scored from it regardless. Remedy:
    --requests or --fault-rate.
  ~ error rate -- Zero failures in 40 requests bounds the
    error rate at roughly 8% with 95% confidence, not at 0%.
    Remedy: --requests.
  ~ 5 caveats on unscored rows (behavior, concurrency, cost,
    latency) -- -v to show.

  Score breakdown:
    latency         14/20     (p95 latency was 7,988ms, policy allows at most 5,000ms)
    cost            -/15      (not measured against this target)
    concurrency     -/15      (no policy threshold reads it)
    contract        8/15      (invalid inputs accepted was 9, policy allows at most 0)
    behavior        35/35

  Score: 81/100  (policy production-default)

Latency findings:
  - p95 7.99s and 0.0% errors across 40 requests, with no
    heavy tail, no unusual call overhead, and no error pattern
    to report.

Cost findings:
  - No cost problems found: 647 input tokens per request with
    no significant fixed prefix.

Concurrency findings:
  - Peak goodput is 4.4 successful req/s at 16 concurrent.
    Past that, added concurrency buys latency and errors
    rather than completed work.

Contract findings:
  - CRITICAL 9 inputs the schema forbids were accepted with a
    success response: missing_required[query],
    null_required[query], wrong_type[query]. Every accepted
    violation is on 'query'. The tool is not validating what
    it declares, so invalid data reaches whatever it writes
    to.

Fault tolerance findings:
  - Injected 20 faults across 93 calls (22%): 6 server_error,
    5 rate_limit, 4 connection_refused, 3 timeout, 2
    malformed.
  - Every one of the 10 disrupted operations recovered within
    2 retries.
  - Under fault the latency probe saw a 20% error rate, p95
    8.03s.

Behavior findings:
  - Every one of the 10 disrupted operations recovered within
    2 retries. That budget is the scanner's, not the target's,
    and is not configurable.

8 findings across 6 probes. Run with --output agents-md to generate a fix guide.

FAIL: score 81 meets pass threshold 75, but 2 checks failed: p95 latency, schema violations accepted.
Biggest gaps: contract (8/15), latency (14/20).

ratemyagent v1.9.0 - pip install ratemyagent - github.com/SMWundefined/RateMyAgent

How to read it:

• Actual sits next to target so the gap is the information.
• n/a means the probe could not measure this target. It is excluded from the score rather than counted as a failure.
• ~ marks a caveat about how strong the evidence is — here, that 10 disrupted operations cannot tell this target apart from the injector's own 91% floor.
• The last two lines are the verdict, because that is what a CI log gets searched for. A score above the pass threshold with a failed check is still a FAIL.

The target above is a built-in mock. Before reading a behavior row on a real MCP server the same way, see Known limitations.

Scan a real target

Scan servers you run, or have permission to test. A scan is load: it calls a tool once per request, ramps concurrency, injects faults and retries what fails.

# Your own MCP server, over stdio
ratemyagent scan --target mcp --uri "stdio://./server.py" \
    --tool search --tool-args '{"query": "hello"}'

# A server you run, over Streamable HTTP
ratemyagent scan --target mcp --uri http://localhost:3001/mcp \
    --header 'Authorization: Bearer $TOKEN'

Every HTTP request carries User-Agent: ratemyagent/<version> (+<repo url>) so the traffic is identifiable in an access log, unless you pass your own --header 'User-Agent: ...'.

Five things to know before scanning something real:

• Pass --tool and --tool-args. Probing calls a tool for real, once per request. Without real arguments, placeholders are synthesized from the schema, and if the server rejects them the scan refuses and exits 2 rather than scoring the rejection.
• Probing a write tool writes. Auto-selection only picks a tool it can establish is read-only; a tool that changes state needs --allow-mutating. Point that at something disposable.
• Credentials: --header for http/sse, --env KEY=VALUE for a stdio server — the MCP SDK does not pass your shell environment to the child process. Values are redacted in every report and export.
• A rate-limited dependency reads as an unreliable one. The scan becomes part of the load. Scale --requests to the quota.
• The concurrency ramp is more than half the traffic. At the defaults it walks 1, 2, 4 and 5 concurrent and sends --requests at each level: 80 of the 140 calls a --requests 20 scan made against a real server, counted from that server's own state. --requests 20 --probes latency,contract,fault,behavior drops it, and costs nothing in the score — no policy threshold reads concurrency, so it is reported and never graded.

Transports: https://host/mcp (Streamable HTTP), stdio://./server.py, and sse+https://host/sse (deprecated by the 2025-06-18 spec).

The detail behind each of these — the refusal messages, the read-only gate, credential redaction, --scan-timeout — is in docs/SCANNING.md.

Validated on two SQLite MCP servers. --verify-tool was run against npx mcp-sqlite@1.0.9 (create_record / read_records) and npx mcp-server-sqlite-npx@0.8.0 (write_query / read_query), each writing an {op_id} into a throwaway database. Both reported duplicate_mutations from the database's own contents — one operation applied twice on each, two on one arm — and a stdlib-only script that re-derives the ids and counts the rows agreed with every number. This is what a plain insert does under at-least-once retry: the reply was dropped after the row was written, the caller retried, and a second row appeared. Neither server is doing anything wrong, and neither finding is a bug report against them — an insert with no idempotency key behaves exactly this way, which is why it is the case worth being able to measure.

Agents (experimental)

New in 1.5.0 and not frozen; the walkthrough above is the fast path. --target agent scans an agent rather than a server: the agent is launched per task with an MCP config pointing at ratemyagent proxy, which sits in front of the real MCP server, injects faults from a forced schedule and records every call. With --verify-tool or --verify-command the scan reads the server's state before and after each task, one task at a time, and joins what the agent claimed with what the record and the state show:

An agent scan gets a verdict only with --verify-tool or --verify-command, every task's state read, and at least one task where a call went unanswered — otherwise nothing tested whether the agent could apply a write twice. Short of that it prints NO VERDICT with the reason, and ci exits 2. A pass whose agent claimed failure on applied work, or success no reply confirmed, prints PASS, UNRECONCILED with the readings and tasks; it is still a pass, and ci exits 0. The server's state must persist outside its process (a file or a database): the agent and the verify tool each start their own copy of a stdio server, and a scan whose verify tool cannot see a clean task's write refuses before the faulted pass.

Scripted fixtures, for a checkout: three agents that never import this package — careful (one idempotency key per operation, growing backoff, honours the hint), blind (no key, no wait) and optimistic (blind, then claims success anyway) — against a server twin whose own ledger records every call as applied or absorbed. Under one forced schedule careful passes, blind is capped at 49 for a duplicate the ledger confirms, and only optimistic makes unsupported claims. From a checkout:

ratemyagent scan --target agent \
    --agent "python tests/fixtures/agents/careful_agent.py" \
    --tasks tests/fixtures/agents/tasks-demo.json \
    --upstream "stdio://python tests/fixtures/event_twin_mcp_server.py --mode append --role {role} --state /tmp/rma-demo.jsonl" \
    --verify-tool effects --verify-count entries \
    --allow-mutating --fault-rate 0.7

Swap careful_agent.py for blind_agent.py or optimistic_agent.py and nothing else.

The evidence: three gates

An agent scan has been run against real agents three ways. Each has its own evidence directory, its own stdlib-only checker that imports nothing from this package, and its own README with the full account. They establish different things, and none subsumes another.

Gate D: examples/phase-d-gate/, with earlier single runs in examples/phase-d/. Gate BD: examples/gate-bd/. Gate S: examples/gate-s/. None of them is a rate: one model (claude-haiku-4-5), one task, five replicates per arm.

What an agent must accept and print is in docs/SCANNING.md, and what this cannot tell you is in docs/LIMITATIONS.md.

How a scan works

Three phases, in order. Phase 2 needs phase 1 to compare against; phase 3 reads what phase 2 recorded.

Phase 1 — Baseline. Latency, cost, concurrency and contract probes measure the target as it is. These are the numbers everything else is compared against.

Phase 2 — Fault injection. A FaultProxy wraps the target and injects faults at a configurable rate. Probes cannot tell they are wrapped, so the same probes run against a sabotaged target and any difference is attributable to the faults.

Phase 3 — Behavior analysis. Reads the trajectory of every operation phase 2 disrupted: did it recover, how long did that take, how many calls did one operation cost. This is the part that is not a load test — it measures behaviour under failure, not failure counts.

Against something that retries — an agent, or a client wrapping a service — the trajectory is the target's. Against a bare server the retry loop belongs to the scanner, so what gets scored there is target survivability.

The contributor-facing walkthrough is in docs/ARCHITECTURE.md.

Scoring

Results are scored 0–100 against a YAML policy. Probes measure; the policy decides.

• Meeting a threshold scores 100 for that check — a threshold is a limit, not a target.
• Missing it decays linearly to 0 at twice the limit, so a near miss and a catastrophe do not score alike.
• A metric the scan could not produce is skipped, not zeroed. Missing evidence is not a failure.
• Passing requires both a score at or above pass_score and no failed check. A failed check also caps the score at 89, or at 49 for a contract crash or a duplicate mutation, so one failure cannot be averaged away. A duplicate-mutation failure needs --verify-tool; without it the check is skipped rather than passed.

# my-policy.yaml
name: my-service
thresholds:
  p95_latency_ms: 3000
  error_rate_max: 0.02
  recovery_rate_min: 0.95
  retry_amplification_max: 1.5
  duplicate_mutation_max: 0
pass_score: 80

ratemyagent policy                                    # show the shipped defaults
ratemyagent scan --target mock --policy my-policy.yaml

Every threshold is optional, and an unknown key is an error rather than silently unscored. Full reference, including the shipped default explained threshold by threshold and how the caps work: docs/POLICY.md.

CI integration

ratemyagent ci --target mcp --uri stdio://./server.py --policy production.yaml
echo $?     # 0 pass, 1 fail, 2 the scan could not run

Exit code 2 matters: a broken scanner is not a failing target, and a gate that cannot tell them apart is not worth having in a pipeline. Failed checks are printed individually, and --scan-timeout bounds the whole run so a hung handshake fails cleanly instead of burning the job's time limit.

ci is the gate; scan is not. scan prints FAIL and still exits 0 — it reports, and a reporting command that exits non-zero breaks every pipeline that runs it for the artifact. Only ci turns the verdict into an exit code, so a gate that greps scan's output for FAIL is not a gate. Both exit 2 when the scan could not run at all, which since 1.4.1 includes a run refused because the target still holds the ids this seed would write, and a run whose --verify-tool was requested but did not measure.

# .github/workflows/reliability.yml
name: reliability

on: [push, pull_request]

jobs:
  scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: astral-sh/setup-uv@v5
        with:
          python-version: "3.12"

      - run: uv pip install --system '.[mcp]'

      - name: Reliability gate
        run: |
          ratemyagent ci \
            --target mcp --uri stdio://./server.py \
            --policy production.yaml \
            --requests 120 --concurrency 16 --fault-rate 0.25 \
            --json-out scan.json

      - uses: actions/upload-artifact@v4
        if: always()
        with:
          name: reliability-scan
          path: scan.json

Use enough requests that the numbers mean something. recovery_rate from the default 20 requests is measured over roughly 4 disrupted operations, which is an anecdote rather than a rate.

AGENTS.md and the report

ratemyagent scan --target mcp --uri stdio://./server.py --output all
# then, in Claude Code, Codex, Cursor or whatever you use:
#   "Read AGENTS.md and fix what it found."

Both markdown outputs are written to be handed straight to a coding agent. AGENTS.md is the fix guide — findings with root causes and copy-pasteable patches. REPORT.md is the evidence behind them — every metric, every probe, and how the scan was run, organized by phase. Hand over the first to get work done, the second when you want the model to check the reasoning rather than trust it. They are about 2,300 and 1,700 tokens, so both fit in any context window.

Each finding states what was observed, why it matters in production, the root cause — weighted toward what AI-generated servers actually get wrong — and a fix naming the tool that failed:

FINDING: 9 schema-forbidden inputs accepted

Your tool declares required fields and types in its JSON Schema but does not enforce them at runtime. This is common in AI-generated MCP servers where the schema is correct but the handler trusts its input. Every field marked "required" needs an explicit check before the handler touches the data, because the calling agent WILL send malformed arguments — that is normal traffic, not an attack.

Suggested fix for tool "search_database":

if "query" not in args or not isinstance(args["query"], str):
    return {"error": "query is required and must be a string"}

Re-scanning into the same file reports what changed since the last scan. Real output is in examples/: a scan of the official mcp-server-git (AGENTS.md, report) and a deliberately broken mock that triggers every finding at once.

Known limitations

Each of these affects scores you can produce today. The full account, with the measurements behind each one, is in docs/LIMITATIONS.md — read it before relying on a number.

• Retry behaviour is not scored against a bare MCP server. A server does not retry; the scanner does, so retry amplification describes RateMyAgent. It is reported, marked n/a, and is scored only against --target agent, where the loop is the agent's.
• Agent scans are experimental and narrow. Tasks run one at a time, and the Retry-After hint reaches an agent in the tool error body, a convention a real client may not read. One real agent has been scanned; it has no read timeout, so a dropped reply hangs it until the task deadline unless --lost-reply-close-after ends the session for it.
• The scan's own clean pass writes to the store your repeats run against. One clean pass, N chaos runs, one persistent store. An agent whose idempotency key derives from the task's content sends the same key every run, and a server that absorbs a repeated key absorbs it across runs — so later runs can apply nothing for a reason that is not about the agent. The scan says so in a caveat and does not repair it; isolating state per run is your fixture's job (how). Since 1.6.2 a run in which every mutating task applied nothing gets NO VERDICT rather than PASS, and ci exits 2 — a coverage rule, not a penalty: no score moves.
• Duplicate mutations need --verify-tool. Left to itself the scan counts calls it re-sent after dropping a reply and reports that as its own; it cannot see whether the target applied them twice. Pass a read-only tool that reports the target's state, with {op_id} in --tool-args, and the metric is measured per operation (how). Without it the check is skipped, not passed — and since 1.4.1 a scan whose oracle was requested but did not measure never prints PASS, and ci exits 2.
• Synthesized arguments are shallow. A scan refuses when the server rejects them, but a tool that accepts a placeholder is profiled on a trivial call.
• Network-backed targets vary run to run. Quote a range from repeated runs, with the count.
• The scan can cause the failure it reports. Against a rate limiter, retries add load. Since 1.0.1 the scan backs off and honours Retry-After, but it does not detect a quota.
• A latency figure describes the path the call took. A cache hit is a real cost, not a round trip to the dependency the tool's name implies.
• Faults are transient. Each attempt is faulted independently; sustained outages are not modelled.

API stability

1.0 means the frozen surface will not break without a major version. It does not mean the findings are finished — the section above says plainly what this tool still cannot measure.

Frozen: scan(), the target adapters, ProbeConfig, Policy, the result shapes (ScanResult, ProbeResult, CheckResult, Caveat), ErrorKind and FaultKind members, the CLI flags, the exit codes, and the eleven metric names a policy threshold reads.

Not frozen: the rest of ProbeResult.metrics, Response.meta, and ProbeConfig.extra keys with no CLI flag behind them. The whole agent path — AgentTarget, --target agent and its flags, ratemyagent proxy, the record format and the agent metrics — is experimental and outside the freeze, and 1.6.0 changed it. FaultKind.RESPONSE_LOST_THEN_CLOSED is the exception: FaultKind members are frozen, adding one is a minor release, and that is why this is 1.6.0. Each is a reporting channel rather than a contract, and anything a consumer comes to depend on gets promoted to a named field by a written procedure rather than by habit.

docs/API-STABILITY.md is the full statement, including the promotion rules and why each unfrozen thing is unfrozen. It ships in the sdist. Wheels carry no docs directory, so from a wheel this section and the file at the matching git tag are the reference.

Roadmap

• Next — ratemyagent chaos for targeted single-fault scenarios; --contract-tools to raise contract coverage above the default three (with a hazard noted in docs/SCANNING.md); a dry-run mode, which needs --verify-tool as its evidence that nothing was applied

• Agents — shipped, and experimental in its API. --target agent (1.5.0) runs an agent through a fault-injecting proxy and counts what each task applied in the upstream's own state. It has a launch contract a hosted CLI can satisfy and a fault a client with no read timeout cannot ignore (1.6.0). Repeats are grouped by where the faults landed, and the agent's own read timeout is measured (1.6.1). There is no PASS over a run that applied nothing (1.6.2), multi-write tasks are read per entry (1.7.5), and faults are counted by what took effect (1.8.0). 1.9.0 is the adoption release: a 15-minute walkthrough, --verify-command for a store no tool reads, runners for the OpenAI Agents SDK and LangGraph, retry keys, a NO VERDICT that names a seed that works, and PASS, UNRECONCILED. Three gates stand behind it: Gate D against Claude Code, Gate BD against an unmodified third-party server, and Gate S across three stacks (evidence).

  What it does not establish. One model, one task per gate, five replicates per arm. None of it is a rate. retry_amplification stays unscored on an LLM, because its denominator is a single clean pass and a model's call count varies between runs.

  Next. Repeat the clean pass as well as the chaos runs, so the denominator has a spread and a scored ratio becomes defensible on an LLM. It doubles the cost of the expensive pass, which is why it is next rather than done.

• v2 — sustained outage windows; historical trending across scans

Deliberately out of scope: web dashboards, continuous monitoring, framework-specific adapters, security scanning, and anything requiring a database.

Contributing

git clone https://github.com/SMWundefined/RateMyAgent.git
cd RateMyAgent

uv venv --python 3.12
uv pip install -e '.[dev]'              # editable, with pytest and ruff

uv run pytest -q                        # prints the count; no network or API keys
uv run ruff check .

Start with docs/ARCHITECTURE.md — it covers the Target interface, the FaultProxy, the trajectory model, and the policy engine, including the parts that are load-bearing and the reasoning behind them.

House rules, in short:

• Every probe needs tests that run without API keys, a network, or an MCP server. Use the mock targets in tests/conftest.py.
• Probes measure, the policy judges. A probe that emits a verdict is a bug.
• The FaultProxy is the only place faults are injected.
• No interactive prompts. This is an SRE tool; it has to stay pipeable.
• Say what you measured, not what you would like to be true. Findings call out thin evidence rather than letting it pass quietly.

License

Apache License 2.0 — see LICENSE and NOTICE.

Apache rather than MIT because it carries an express patent grant from contributors, and section 5 states that contributions are offered under the same terms unless you say otherwise. MIT is silent on both. Equally permissive; less for a legal review to work out.