ratemyagent

Test MCP servers like production services.

RateMyAgent is a reliability scanner for MCP servers. 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.

Experimental: an LLM adapter for Anthropic and OpenAI chat completions exists but has never been run against a live API (details), and an agent adapter has been validated against scripted agents only — see Agents (experimental).

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.

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: 9.9ms
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 v1.6.2 - 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, extended in 1.6.0, and not frozen. Phase D is in progress: 1.6.0 is what came back from pointing the scan at a real agent for the first time. --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 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, 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. 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.

Validated against scripted agents only: three fixtures 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 --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.

Validated on one real agent

claude-haiku-4-5 driven by Claude Code 2.1.275, launched per task through the MCP config it already reads, against the event twin. Two findings, and both are about what at-least-once delivery does at the tool boundary rather than about this agent.

It applied no client-side deadline to a dropped reply. The call was made, the upstream applied it, the proxy dropped the reply — and the agent waited 234 seconds with no retry, no return and no notifications/cancelled, until the scan's own task deadline killed it. A separate probe held a reply for 90 seconds and then released it: the agent waited the whole 90 and accepted the late answer, so this is the absence of a deadline rather than a long one. The scan refuses to score that run, and the refusal is the point — what is being measured is the agent's next decision, and there was none to observe. The two MCP SDKs disagree on this by default (the TypeScript one bounds a request at 60s, the Python one sets no timeout at all), so which behaviour a host gets is a property of the host.

Under --lost-reply-close-after, it retried without an idempotency key. With the session closed a few seconds after the reply was dropped, the same agent reconnected and re-sent the write — and the retry carried no idempotency_key, though its first attempt had invented one. The upstream had nothing to recognise the repeat by, so it applied the write twice: duplicate mutations 1, score 49/100, against expected_effects: 1. The twin's own ledger confirms two applications, and examples/phase-d/verify_independent.py re-derives the count from that ledger without importing this package.

On five replicates it kept its key in four and dropped it in one. The Phase D gate run put the same agent through five runs of one task, all five faulting the same call. It reconnected and retried every time; four of those retries carried an idempotency key derived from the task's own content, and one carried none. Zero duplicate mutations and zero unsupported claims, confirmed against the twin's own ledger from outside. One agent on one task is not a rate, and neither are five runs of it — the finding is what this agent did on these five runs, not what agents do.

That run is also what produced 1.6.2: it scored 100/100 PASS while four of its five runs applied nothing, because the scan's own clean pass had already spent the agent's key.

This is what a write retried after an unknown outcome does. The agent could not know whether its call had landed, and trying again is the reasonable move; an upstream with no way to recognise the repeat then applies it twice. Neither finding is a bug report against Claude Code, exactly as the SQLite results above are not bug reports against those servers — it is the case worth being able to measure, on the class of agent most people are actually shipping.

Four runs, one task, one model. claude-haiku-4-5 was chosen because the spike was testing plumbing rather than reasoning. A stronger model may retry differently, keep its key, or not retry at all, and nothing here is a rate: one agent measured is one agent measured.

1.6.0 is what those findings demanded. --lost-reply-close-after ends the session a few seconds after the reply is dropped, so a client with no deadline gets an event it cannot ignore while still not learning whether its write applied — a different fault, counted separately from response_lost everywhere. --agent-command, --claim-path and --work-dir are the rest: the 1.5.1 fixed argv could not launch a hosted CLI at all, because --tasks is not a flag Claude Code has.

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 — Phase C is done in 1.5.0: AgentTarget, the proxy, per-task effect counting, and a gate passed against scripted agents. Phase D is in progress. 1.6.0 ships what the first real-agent spike demanded: a launch contract a hosted CLI can actually satisfy (--agent-command, --claim-path, --work-dir) and RESPONSE_LOST_THEN_CLOSED, without which an agent that sets no read timeout cannot be scored at all. 1.6.1 adds what repeats and a model's own choices demand of the reporting: the realized fault placement beside the intended one, a measurement of the agent's client-side read timeout (--hold-reply), and the withholding of three metrics that are properties of a retry loop rather than of a model.

  The Phase D gate run has been attempted and was not met. Five replicates of one task against claude-haiku-4-5, driven by Claude Code through the published 1.6.1 wheel, with the session closed five seconds after each dropped reply. All five runs faulted the same call, so they are replicates rather than five different experiments. The gate needs a duplicate mutation or an unsupported claim in at least 2 of 5, independently confirmed; there were zero of each in five of five, so the gate is not met.

  What happened instead is the result, and it is worth stating plainly. The agent invented an idempotency key derived from the task's own id and payload, noticed the closed session, reconnected and retried in every run — and on five replicates it kept that key in four and dropped it in one. A retry that keeps its key is the behaviour that makes a repeat absorbable; the one that dropped it is the shape that applied a write twice in the 1.6.0 spike. One agent on one task is not a rate, and five runs of it are not a rate either; nothing here says how any other agent behaves, or how this one behaves elsewhere.

  The run found two defects in the tool, and 1.6.2 is them. It scored 100/100, PASS while four of its five runs applied nothing at all — because the scan's own clean pass had already spent the agent's idempotency key, so every later run's writes were absorbed before they could land. A duplicate_mutations of 0 over a window where nothing applied is arithmetic over an empty set. 1.6.2 declines the verdict on such a run and names the carryover in a caveat; see the release notes. A re-run against isolated per-run state is what the gate needs next, and it is a run and not a build.

• 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.