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134,637 tools. Last updated 2026-05-22 23:53

"namespace:io.github.Just-Fix" matching MCP tools:

  • DRIFT CHECK: Run a read-only drift detection check Checks whether deployed infrastructure has drifted from the expected Terraform state. This is a read-only operation — it does NOT modify any infrastructure. Returns job_id. Use tflogs to stream the drift check results. SINGLE-FLIGHT: only one TF job per session at a time. If another job is already in flight, tfdrift returns tf_job_conflict with the live job_id — attach with tfstatus/tflogs, or pass force_new=true to override. REQUIRES: session_id from convoopen response (format: sess_v2_...). PREREQUISITE: The session must have a prior deployment with a project_id. OPTIONAL: force_new (boolean, default false) - bypass the single-flight guard. Use only when the existing run is provably wedged. If drift is detected, the user can either fix the drift or use tfdeploy(ignore_drift=true) to proceed.
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  • Replay the sandbox test for one or more suites against captured mocks — re-runs the suite's steps against the dev's locally-running app while keploy serves outbound calls (DB, downstream HTTP, etc.) from the captured mocks. Use this when the dev says "replay", "run my sandbox tests", "integration-test", "check if mocks still match" — keywords "sandbox" / "replay" / "mocks" / "integration-test" all map here. Also the REPLAY STEP of FROM-SCRATCH: call this LAST (after create_test_suite + record_sandbox_test) to give the dev the whole-app regression picture against the freshly captured mocks. Output produces a SANDBOX RUN REPORT — it answers "does the suite still hold up against its captured baseline?". ═══════════════════════════════════════════════════════════════════ DISAMBIGUATION — pick this tool vs. replay_test_suite: ═══════════════════════════════════════════════════════════════════ USE replay_sandbox_test (THIS TOOL) when the dev says: * "run my sandbox tests" / "replay my sandbox tests" * "integration-test my app" / "run the integration tests" * "check if my mocks still match" / "replay against the captured mocks" * "rerun my sandbox suite" (with the word "sandbox") Trigger keyword: an explicit "sandbox" / "replay" / "mocks" / "integration-test" — silent signal that the dev wants captured-mock replay, NOT live-app execution. USE replay_test_suite INSTEAD when the dev says: * "run the test suite" / "run my test suites" (bare — no "sandbox") * "execute test suite X" / "run suite 810d3ebe…" * "test the suite again" / "smoke test against the live app" Bare verbs ("run / test / execute") applied to "the suite" without the word "sandbox" mean LIVE-APP execution, NOT captured-mock replay. replay_test_suite hits the dev's running localhost app directly via HTTP — no docker spin-up, no mocks. After a record_sandbox_test run, the natural next step is THIS tool (replay against the just-captured mocks). After create_test_suite / update_test_suite, the natural next step is replay_test_suite (validate against the live app). When the dev's verb is bare and the prior turn doesn't make the intent obvious, ASK rather than picking sandbox-replay silently — code-change regressions can hide under "mock didn't match" failures. ═══════════════════════════════════════════════════════════════════ DISCOVERY — when the dev hands you a bare suite_id with no app_id / branch_id: ═══════════════════════════════════════════════════════════════════ Suites live on a (app_id, branch_id) tuple. A bare suite_id has NO on-disk hint about which app or branch holds it; you have to RESOLVE both before calling this tool. Walk these steps in order — STOP as soon as getTestSuite returns 200: 1. Detect the dev's git branch: Bash `git rev-parse --abbrev-ref HEAD` in app_dir. If exit non-zero / output is "HEAD" → not a git repo / detached HEAD; ASK the dev for the Keploy branch name. 2. Resolve candidate apps via the cwd basename: Bash `basename $(pwd)` → call listApps with q=<basename>. Usually 1–2 candidates. If 0 → ASK; if >1 → walk every candidate in step 4. 3. For each candidate app, call list_branches({app_id}) and find the branch whose `name` matches the git branch from step 1. That gives you {branch_id}. If no match → not this app, try next. 4. Verify with getTestSuite({app_id, suite_id, branch_id=<from step 3>}). 200 → resolved; 404 → wrong app/branch, try next. 5. If steps 2–4 exhaust, walk every OPEN branch on each candidate app via list_branches → getTestSuite. Then try main (branch_id omitted). If still nothing → ASK the dev for the {app_id, branch_id} pair. After resolving once in a session, REUSE the {app_id, branch_id} for subsequent suite-targeted calls; don't re-walk discovery for every action. SCOPE — whole-app vs single-suite: * Default: LEAVE suite_ids UNSET → the tool resolves "every suite for the app that has a sandbox test (test_set_id populated)" and replays them all. Use this for "run my sandbox tests" / "check if my tests still pass" — whole-app regression. New suites auto-pick up. * Single / subset: PASS suite_ids when the dev names specific suites — "replay sandbox test for suite 810d3ebe-…", "replay only the auth suite", "run suite X and Y". The tool validates each requested id is actually a suite with a sandbox test (has test_set_id); an unlinked id gets a precise "record first" error instead of an opaque downstream CLI failure. This tool resolves the app, picks the suite set per the rule above, and returns a single playbook that drives the replay for them. It does NOT record. WHAT THIS TOOL DOES INTERNALLY (so you don't have to): 1. Resolves app_id — use the explicit app_id if the caller has one; otherwise pass app_name_hint (usually the cwd basename) and the server does listApps with a substring match. Multiple matches → error listing them; zero matches → error suggesting the dev generate a suite first. 2. Lists test suites for the app, keeps only those with a non-empty test_set_id. Zero linked → typed "no linked sandbox tests" error. 3. If suite_ids was passed, validates every requested id is in the linked-suites set; unlinked ids → typed error pointing to record_sandbox_test. 4. Returns the headless playbook — walk it exactly: spawn CLI in background, tail the progress file (PID-alive guard built in), read the terminal event, fetch the report. No separate cleanup step — the CLI exits on its own. ===== PREREQUISITES ===== (Same as record_sandbox_test — if you just recorded, you already have them. Same docker-compose network rule applies: use the same compose file + service, stop the app service before calling, leave deps running.) - app_command: shell command that starts the dev's app (e.g. "docker compose up producer"). - app_url: base URL the app listens on, e.g. http://localhost:8080. - app_dir: absolute path to repo root. - container_name if app_command is docker-compose. - keploy binary on PATH. If `which keploy` returns nothing, install it before calling this tool with: `curl --silent -O -L https://keploy.io/install.sh && source install.sh`. ===== AFTER CALLING — walk the playbook ===== Same headless playbook shape as record_sandbox_test: spawn `keploy test sandbox --cloud-app-id …` in the background via Bash, poll `tail -n 1 $PROGRESS_FILE` repeatedly (no sleep loops; the wait_for_done step has a built-in `kill -0 $KEPLOY_PID` guard so the loop exits if the CLI dies silently), read the terminal NDJSON event (phase=done, data.ok, data.test_run_id), and — if ok=true — call get_session_report(app_id, test_run_id) with verbose=true at the end. No separate cleanup step needed; the CLI exits cleanly once phase=done is written. ===== MANDATORY OUTPUT — Phase 3 section ===== Your final message to the dev MUST contain a section with this exact heading (do NOT merge with Phase 2; do NOT compress the failed-steps table even when failures are homogeneous): ### Phase 3 — Sandbox run report Under it, emit the uniform three-subsection format owned by get_session_report: (i) per-suite table — one row per suite in per_suite, passing suites included, columns = Suite name | passed/total steps. (ii) failed-steps table — ONE ROW per entry in failed_steps[], columns = Suite | Step name | Method + URL | Expected → Actual status | mock_mismatch y/n. Never collapse rows. (iii) Diagnosis + Recommendation (see get_session_report description for case-specific rules around mock_mismatch_dominant, repo-diff inspection, and the SKIP / FIX-CODE / FIX-TEST branching for fix-it follow-ups). Do NOT print aggregate step totals across suites — they mix unrelated suites and hide where damage actually is. ===== ROLLUP LINE ===== Close the message with a final one-line rollup paragraph (no heading), in addition to the three phase sections. Mention the TOTAL number of suites replayed (which may exceed the count created in this session, because replay_sandbox_test covers every linked suite the app has). Example: "_Rollup: inserted 4 suites, 4/4 with sandbox tests after record, 3/4 suites passed sandbox replay across the app's 6 linked suites — 1 failure is likely keploy egress-hook, file an issue with the IDs above._" ===== DO NOT ===== * DO NOT call update_test_suite or record_sandbox_test after this. The dev said RUN, not REFRESH. * DO NOT fall back to raw keploy CLI (`keploy test …`) if the MCP tool drops mid-flow — CLI runs test-sets directly and does NOT write results back to the MCP-visible TestSuiteRun. See MCP DISCONNECT RECOVERY in the top-level instructions.
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  • DRIFT CHECK: Run a read-only drift detection check Checks whether deployed infrastructure has drifted from the expected Terraform state. This is a read-only operation — it does NOT modify any infrastructure. Returns job_id. Use tflogs to stream the drift check results. SINGLE-FLIGHT: only one TF job per session at a time. If another job is already in flight, tfdrift returns tf_job_conflict with the live job_id — attach with tfstatus/tflogs, or pass force_new=true to override. REQUIRES: session_id from convoopen response (format: sess_v2_...). PREREQUISITE: The session must have a prior deployment with a project_id. OPTIONAL: force_new (boolean, default false) - bypass the single-flight guard. Use only when the existing run is provably wedged. If drift is detected, the user can either fix the drift or use tfdeploy(ignore_drift=true) to proceed.
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  • Validate scene_data before generating 3D code. Runs 12 structural checks across 4 categories: S — Structure (4 rules): scene_id, objects array, camera validity O — Objects (5 rules): ids, positions, frustum bounds, overlap, pending synthesis contracts L — Lighting (2 rules): non-ambient light presence, intensity range A — Animation(2 rules): target_id resolution, config fields Severity levels: error → blocks codegen. Must fix before generate_r3f_code. warn → does not block. Review before proceeding. Returns is_valid: true only when zero "error" rules fail. Returns next_step string with exact instruction for what to do next. Call this tool AFTER generate_scene and BEFORE synthesize_geometry. If is_valid is false, call edit_scene to fix errors, then re-run validate_scene before proceeding to codegen.
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  • Pro/Teams — second-pass adversarial certification of an architect.validate run that scored production_ready (A or B first-pass tier). ON CLIENT TIMEOUT — DO NOT RETRY THIS TOOL. Long-running LLM call (60-180s typical; exceeds Claude Code's ~60s idle budget); MCP clients commonly close the call before the server returns. Retrying re-runs the LLM call AND burns one of your 3 cert retry-budget attempts. RECOVERY: the run_id is emitted in the FIRST notifications/progress event at t=0s — capture it. On timeout, call `me.validation_history(run_id='<that-id>')` to fetch the persisted cert verdict; the server-side run completes independently. Mints the certified production_ready badge when both reviewers sign off; caps the run to C/emerging when the second pass surfaces a missed production_blocker. MANDATORY DOCTRINE RULE (load-bearing): the badge certifies the EXACT code that produced the validate run_id, NOT 'this codebase' in general. If you modify, fix, or iterate the code between architect.validate and architect.certify — even a single character — cert rejects with code_fingerprint_mismatch. Fixing the code voids the run. The recovery path is always: edit code → architect.validate → fresh run_id → architect.certify on the fresh run. Do NOT cert from a stale run_id after iteration; ask the user to re-validate first. WHEN TO CALL: only after architect.validate returned tier=production_ready AND the user wants the certified badge AND the code has not been touched since the validate run. NOT for tier=draft/emerging/not_applicable runs (typed rejections fire — see below). NOT idempotent across attempts: each call is one of the 3 attempts in the retry budget. BEHAVIOR: atomic one-shot single LLM call, ~60-180s server-side at high reasoning effort (small payloads finish faster; observed p99 ~250s; server-side budget is 20 min, ~5× observed max). Exceeds typical MCP-client tool-call idle budget (~60s in Claude Code), so the FIRST notifications/progress event fires at t=0 carrying the run_id. The run is atomic by contract — no in_progress lifecycle, no cancellation, no resume. Updates the persisted run's result_json (public review URL + me.validation_history(run_id=...) reflect the cert outcome). ELIGIBILITY GATE (typed rejection enum on failure): caller must own the run, tier=production_ready, less than 24h old, not already certified, within cert retry budget (max 3 attempts), no other cert call in flight for the same run_id, code fingerprint must match the validated code, AND the submitted payload must be cert-payload-complete (see Payload Completeness below — cert rejects pre-LLM with `payload_incomplete` when an imported module's surface isn't visible in the validate payload that produced this run_id). Rejection reasons (typed Literal): auth_required, paid_plan_required, run_not_found, not_run_owner, not_eligible_tier, not_agentic_component (tier=not_applicable runs), already_certified, certification_age_exceeded, retry_budget_exhausted, code_fingerprint_mismatch, code_fingerprint_missing, code_not_on_file (caller omitted `code` argument AND the 24h cert-retry hold for this run has expired or was never written. Recovery: re-run architect.certify from the same MCP session that ran architect.validate, passing the code explicitly — the server never persists code by design), payload_incomplete (submitted/validated payload imports modules whose contents aren't visible — cert refuses pre-LLM to prevent a false-precision downgrade. Recovery: re-validate with verbatim public-surface stubs for every imported module, then re-cert on the fresh run_id. Empirically validated: PR #157 iter8/iter9 cert rejections were exactly this class — code on disk was correct, the submitted payload merely omitted module visibility), cert_consensus_score_below_threshold (consensus_median<75 — consensus runs only), cert_consensus_unstable_blocker (any principle mode_stability<80% — consensus runs only), run_state_corrupt, cert_persistence_failed, cert_in_flight (a prior architect.certify call on this run_id is still running. Poll me.validation_history for the verdict; do not retry until it resolves). PAYLOAD COMPLETENESS (load-bearing for cert eligibility): the cert reviewer reads the EXACT payload that produced the validate run_id. Imported modules whose surface isn't present in the payload cause pre-LLM `payload_incomplete` refusal. Avoidance — when validating with intent to cert, bundle public-surface stubs for every imported module: `from sqlalchemy.exc import SQLAlchemyError` → include a stub class; `from app.db import models` → include a `class models:` namespace stub with the columns/methods you reference; module-level imports of `dataclass`, `Literal`, `json`, `datetime`, `timezone` MUST also be in the payload (cert correctly catches when they're omitted — code would NameError on import). 'Submit Like Production': the payload should be the code as it would actually run, not a compressed sketch. PRE-LLM REJECTION AUDIT TRAIL: when cert rejects before the LLM call (payload_incomplete, code_fingerprint_mismatch, etc.), `certification_attempts=[]` on the response — no attempt landed in the retry budget, no LLM hop occurred. The rejection envelope's `rejection_reason` + `guidance` are the actionable surface. (Audit-trail UI surfacing of pre-LLM rejections is tracked in the platform self-audit set as anomaly #5; out of scope for the cert tool itself.) INPUTS: re-send the SAME code that produced the run_id (the architect persists findings + recommendations, never code, by design — privacy-preserving). Server compares the submitted code's SHA-256 fingerprint to the stored fingerprint and rejects mismatches. Auth: Bearer <token>, Pro or Teams plan required. UK/EU data residency (Cloud Run europe-west2). Code processed transiently by OpenAI (no-training-on-API-data) and dropped; payloads JSON-escaped + delimited as inert untrusted data — prompt-injection inside code is ignored. If the cert call fails outright (provider error, persistence error), a fresh architect.certify is the recovery path; the eligibility gate enforces the 3-attempt retry budget. For long-running cert workflows the answer is to re-validate, not to make this tool stateful. OUTCOMES: certification_status ∈ {confirmed_production_ready (badge mints), downgraded_to_emerging (cert review surfaced a missed production_blocker, tier capped at C/emerging), unavailable_provider_error (LLM call failed, retry within budget)}. Cert findings + summary + attempt history surfaced on the persisted run for full inspectability.
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  • Returns copy-paste-ready fix recommendations (nginx, Apache, DNS, shell) for the issues found on a domain the caller has already paid for — either an active Monitor/Compliance subscription covering the domain, OR a purchased one-off Report for the domain. Each recommendation carries a stable issue_id, a priority (high/medium/low), a title, prose instructions, one or more config snippets with the target domain already interpolated, a verify command, and a category tag. Use this when the user asks how to fix an issue, wants the exact config to apply, or needs to verify a fix worked. Pass the optional issue_id to scope the response to one specific finding. The response is read-only — this tool NEVER triggers a fresh scan; fixes are computed from the most recent stored scan (including the Report-included re-scan if that was used). Do NOT use this for domains the caller hasn't purchased coverage for — you'll get an upgrade_required error that links to the pricing page. Do NOT use this to run or trigger a scan; call scan_domain for anonymous checks. Requires a valid API key.
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Matching MCP Connectors

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  • Record mocks for V1 repo-mode API tests using the V1-native CLI command `keploy sandbox local record`. Runs the dev's app under the keploy eBPF agent, drives the V1 chained-CRUD tests from `keploy/api-tests/<resource>/test.yaml`, captures every outbound call (DB queries, Redis ops, downstream HTTP) as mocks, and lays them out at `<app_dir>/keploy/<suite-name>/{tests/, mocks.yaml, config.yaml}` in the standard OSS test-set tree. On success, mocks upload to the Keploy canonical pool by content hash; the hash lands in config.yaml so a teammate's later replay fetches the same bytes. CRITICAL — DO NOT CONFUSE WITH `keploy record sandbox`: * `keploy sandbox local record` (V1, repo-mode) ← this is what the playbook below uses * `keploy record sandbox` (legacy, cloud-mode) ← DO NOT call this for V1 The two are entirely different commands. Cloud-mode requires server-side suites (queried via --suite-ids) — V1 repo-mode reads tests from the local filesystem and never registers them in the cloud. If the dev is in repo storage mode (verify via devloop_resolve_storage's source=persisted, mode=repo), V1 is the ONLY correct sandbox path. STRICT — TIME-FREEZING DOES NOT APPLY TO RECORD. Recording MUST use the dev's regular (prod) Dockerfile or native binary. NEVER spawn the app via Dockerfile.keploy / "-f docker-compose.keploy.yml" / "-tags=faketime" build during record. The faketime binary writes wrong timestamps into captured mocks (it reads time from the offset file, not the wall clock) and the entire capture becomes corrupt — recovery requires re-recording from scratch with the prod binary. If a previous replay failed with expired-JWT and the dev wants to "fix" it, the fix is to re-RUN the replay with --freezeTime, NOT to re-record. The recorded mocks captured against the prod binary are exactly what replay's clock-rewind is designed to validate; touching the record path defeats the whole mechanism. ONLY call this with an explicit dev opt-in. The valid triggers: * Dev directly asks ("capture mocks", "sandbox record", "rerecord the users mocks"). * Post-resource menu (Step 5 of devloop_generate_resource_flow) — dev picks "Capture mocks so CI runs in seconds". * get_session_report shows mock_mismatch_dominant=true AND the dev says yes to your "rerecord?" prompt. Pre-conditions: * Dev's app must NOT already be running (keploy spawns its own copy of the app under the agent's eBPF hooks via the -c command). If a server is up at the target port, KILL IT first or the agent's network capture won't see the traffic. * Real downstream deps (MySQL, Redis, Kafka, etc.) MUST be running — the capture proxies through to them on first contact so the recorded mocks contain real responses. * The test YAML must exist at <app_dir>/keploy/api-tests/<resource>/test.yaml. Returns a playbook for `keploy sandbox local record` with the V1 flag surface: --test-dir, --app-url, -c (spawn command), --container-name (docker-compose only), --skip-mock-upload (offline), --skip-report-upload (offline). Mocks land per-suite at keploy/<suite-name>/. NDJSON progress at --progress-file for the standard tail-til-done loop.
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  • Share a solution. Call after the user confirms they want to contribute. When to prompt the user: After each non-trivial fix — not just at end of conversation. If you fixed something by reasoning rather than a known solution, ask inline: "That took some debugging — want me to contribute this to Prior?" Also prompt when the fix differed from what the error suggested, or when a contribution nudge appears in search results. Before submitting, read prior://docs/contributing for field guidance. Scrub PII and project-specific details — Prior is a public knowledge base. Write for developers on unrelated projects, not your team. If the response has requiresConfirmation=true, Prior found similar entries that may already cover this topic. Review them — if they solve the problem, don't re-contribute. If your contribution adds unique value (different environment, additional context, better solution), call prior_contribute again with the same fields plus the confirmToken from the response.
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  • Edit an existing test suite — change one or more step bodies, assertions, headers, or remove/add steps. Returns a playbook that delegates to `keploy update-test-suite`, which validates the new state (static structural checks + 2 live runs for idempotency + GET-coupling check) and snapshot-replaces the suite via api-server. POST-EDIT BEHAVIOUR: any structural change here (step method/url/body/headers/extract/assert, or add/delete steps) AUTOMATICALLY clears the suite's sandbox test server-side — the suite comes back as linked=false. Call record_sandbox_test on the updated suite before any sandbox replay; otherwise replay_sandbox_test will 400 with "no sandboxed tests". Cosmetic-only edits (name, description, labels) preserve the sandbox test. ═══════════════════════════════════════════════════════════════════ FETCH-FIRST RULE — required for the edit to be accepted: ═══════════════════════════════════════════════════════════════════ The api-server's replace handler rejects updates that preserve ZERO step IDs from the existing suite ("full rewrite, not an edit"). To make a real edit: 1. Call getTestSuite first (or use download_recording / get_app_testing_context if you already have the suite). Capture each existing step's "id" field. 2. Compose your new steps_json INCLUDING the existing "id" on every step you want to KEEP or EDIT. Omit "id" only on steps you're ADDING. Drop a step entirely from steps_json to DELETE it. 3. Call this tool with that merged steps_json. If you author a fresh JSON without the existing step IDs, the server rejects it with "preserves no steps from the existing suite". When that happens, your two options are: (a) re-author with IDs preserved (preferred — keeps history), or (b) call delete_test_suite then create_test_suite (loses history, fresh suite_id). ═══════════════════════════════════════════════════════════════════ DISCOVERY — when the dev hands you a bare suite_id with no app_id / branch_id: ═══════════════════════════════════════════════════════════════════ Suites live on a (app_id, branch_id) tuple. A bare suite_id has no on-disk hint about which app or branch holds it; you have to RESOLVE both before calling this tool. Walk these steps in order — STOP as soon as getTestSuite returns 200: 1. Detect the dev's git branch: Bash `git rev-parse --abbrev-ref HEAD` in app_dir. If exit non-zero / output is "HEAD" → not a git repo / detached HEAD; ASK the dev for the Keploy branch name. 2. Resolve candidate apps via the cwd basename: Bash `basename $(pwd)` → call listApps with q=<basename>. Usually 1–2 candidates. If 0 → ASK; if >1 → walk every candidate in step 4. 3. For each candidate app, call list_branches({app_id}) and find the branch whose `name` matches the git branch from step 1. That gives you {branch_id}. If no match → not this app, try next. 4. Verify with getTestSuite({app_id, suite_id, branch_id=<from step 3>}). 200 → resolved; 404 → wrong app/branch, try next. 5. If steps 2–4 exhaust, walk every OPEN branch on each candidate app, then try main (branch_id omitted). If still nothing → ASK the dev for the {app_id, branch_id} pair. The getTestSuite call in step 4 is the one whose response you also use to capture every step's existing "id" for the FETCH-FIRST RULE above — so step 4 is actually a 2-for-1: discovery AND fetch-first happen on the same call. After resolving once in a session, REUSE the {app_id, branch_id} for subsequent suite-targeted calls; don't re-walk discovery for every action. ═══════════════════════════════════════════════════════════════════ INPUTS ═══════════════════════════════════════════════════════════════════ * app_id (required) — Keploy app id * suite_id (required) — UUID of the suite to update * branch_id (required) — Keploy branch UUID (resolve via the two-step flow before calling) * steps_json (required) — JSON array of the FULL desired step list. Each kept step MUST carry the existing "id". Same step shape as create_test_suite (response, extract, assert, etc — all static structural checks apply). * name / description / labels (optional) — overrides for top-level suite metadata * app_url (required) — base URL of the dev's running local app, e.g. http://localhost:8080. The CLI fires the new state TWICE against this for the idempotency check + GET-coupling check. * app_dir (optional) — repo root the CLI cd's into; defaults to "." ═══════════════════════════════════════════════════════════════════ HOW THIS TOOL WORKS ═══════════════════════════════════════════════════════════════════ This tool DOES NOT call api-server itself. It returns a 3-step playbook for you (Claude) to walk via Bash — same shape as create_test_suite: 1. Write merged JSON to a temp file. 2. Run `keploy update-test-suite --suite-id <id> --file <path> --branch-id <uuid> --base-url <url>` — runs every static structural check, fires the new state twice locally, applies the GET-coupling check, then POSTs the snapshot-replace. 3. Cleanup the temp file. Walk the playbook in order. If step 2 exits non-zero, surface stdout to the dev — it has the rule violation / failure detail. OUTCOMES the AI should recognize: * Exit 0 + stdout has "✓ suite updated:" + "View:" line → success. Surface the View URL to the dev. * Exit 1 + "preserves no steps from the existing suite" → fetch-first rule was missed. Re-author with step IDs preserved (or call delete_test_suite + create_test_suite as the documented escape hatch). * Exit 1 + structural-check violations → fix the suite per the violation messages, then REWRITE the suite file via Bash and RE-RUN this CLI command directly. DO NOT call update_test_suite again to retry — the playbook + file path are already valid; only the JSON content needs revision. The validator output includes a canonical step skeleton on structural failures. * Exit 2 + "couldn't reach the dev's app" → ensure the app is up at app_url and retry. PREREQUISITES the playbook assumes: * The dev's app is up and reachable at app_url. * `keploy` binary is on PATH. If missing, install before calling this tool: `curl --silent -O -L https://keploy.io/install.sh && source install.sh`. * Either ~/.keploy/cred.yaml exists or KEPLOY_API_KEY is exported.
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  • Pro/Teams — second-pass adversarial certification of an architect.validate run that scored production_ready (A or B first-pass tier). ON CLIENT TIMEOUT — DO NOT RETRY THIS TOOL. Long-running LLM call (60-180s typical; exceeds Claude Code's ~60s idle budget); MCP clients commonly close the call before the server returns. Retrying re-runs the LLM call AND burns one of your 3 cert retry-budget attempts. RECOVERY: the run_id is emitted in the FIRST notifications/progress event at t=0s — capture it. On timeout, call `me.validation_history(run_id='<that-id>')` to fetch the persisted cert verdict; the server-side run completes independently. Mints the certified production_ready badge when both reviewers sign off; caps the run to C/emerging when the second pass surfaces a missed production_blocker. MANDATORY DOCTRINE RULE (load-bearing): the badge certifies the EXACT code that produced the validate run_id, NOT 'this codebase' in general. If you modify, fix, or iterate the code between architect.validate and architect.certify — even a single character — cert rejects with code_fingerprint_mismatch. Fixing the code voids the run. The recovery path is always: edit code → architect.validate → fresh run_id → architect.certify on the fresh run. Do NOT cert from a stale run_id after iteration; ask the user to re-validate first. WHEN TO CALL: only after architect.validate returned tier=production_ready AND the user wants the certified badge AND the code has not been touched since the validate run. NOT for tier=draft/emerging/not_applicable runs (typed rejections fire — see below). NOT idempotent across attempts: each call is one of the 3 attempts in the retry budget. BEHAVIOR: atomic one-shot single LLM call, ~60-180s server-side at high reasoning effort (small payloads finish faster; observed p99 ~250s; server-side budget is 20 min, ~5× observed max). Exceeds typical MCP-client tool-call idle budget (~60s in Claude Code), so the FIRST notifications/progress event fires at t=0 carrying the run_id. The run is atomic by contract — no in_progress lifecycle, no cancellation, no resume. Updates the persisted run's result_json (public review URL + me.validation_history(run_id=...) reflect the cert outcome). ELIGIBILITY GATE (typed rejection enum on failure): caller must own the run, tier=production_ready, less than 24h old, not already certified, within cert retry budget (max 3 attempts), no other cert call in flight for the same run_id, code fingerprint must match the validated code, AND the submitted payload must be cert-payload-complete (see Payload Completeness below — cert rejects pre-LLM with `payload_incomplete` when an imported module's surface isn't visible in the validate payload that produced this run_id). Rejection reasons (typed Literal): auth_required, paid_plan_required, run_not_found, not_run_owner, not_eligible_tier, not_agentic_component (tier=not_applicable runs), already_certified, certification_age_exceeded, retry_budget_exhausted, code_fingerprint_mismatch, code_fingerprint_missing, code_not_on_file (caller omitted `code` argument AND the 24h cert-retry hold for this run has expired or was never written. Recovery: re-run architect.certify from the same MCP session that ran architect.validate, passing the code explicitly — the server never persists code by design), payload_incomplete (submitted/validated payload imports modules whose contents aren't visible — cert refuses pre-LLM to prevent a false-precision downgrade. Recovery: re-validate with verbatim public-surface stubs for every imported module, then re-cert on the fresh run_id. Empirically validated: PR #157 iter8/iter9 cert rejections were exactly this class — code on disk was correct, the submitted payload merely omitted module visibility), cert_consensus_score_below_threshold (consensus_median<75 — consensus runs only), cert_consensus_unstable_blocker (any principle mode_stability<80% — consensus runs only), run_state_corrupt, cert_persistence_failed, cert_in_flight (a prior architect.certify call on this run_id is still running. Poll me.validation_history for the verdict; do not retry until it resolves). PAYLOAD COMPLETENESS (load-bearing for cert eligibility): the cert reviewer reads the EXACT payload that produced the validate run_id. Imported modules whose surface isn't present in the payload cause pre-LLM `payload_incomplete` refusal. Avoidance — when validating with intent to cert, bundle public-surface stubs for every imported module: `from sqlalchemy.exc import SQLAlchemyError` → include a stub class; `from app.db import models` → include a `class models:` namespace stub with the columns/methods you reference; module-level imports of `dataclass`, `Literal`, `json`, `datetime`, `timezone` MUST also be in the payload (cert correctly catches when they're omitted — code would NameError on import). 'Submit Like Production': the payload should be the code as it would actually run, not a compressed sketch. PRE-LLM REJECTION AUDIT TRAIL: when cert rejects before the LLM call (payload_incomplete, code_fingerprint_mismatch, etc.), `certification_attempts=[]` on the response — no attempt landed in the retry budget, no LLM hop occurred. The rejection envelope's `rejection_reason` + `guidance` are the actionable surface. (Audit-trail UI surfacing of pre-LLM rejections is tracked in the platform self-audit set as anomaly #5; out of scope for the cert tool itself.) INPUTS: re-send the SAME code that produced the run_id (the architect persists findings + recommendations, never code, by design — privacy-preserving). Server compares the submitted code's SHA-256 fingerprint to the stored fingerprint and rejects mismatches. Auth: Bearer <token>, Pro or Teams plan required. UK/EU data residency (Cloud Run europe-west2). Code processed transiently by OpenAI (no-training-on-API-data) and dropped; payloads JSON-escaped + delimited as inert untrusted data — prompt-injection inside code is ignored. If the cert call fails outright (provider error, persistence error), a fresh architect.certify is the recovery path; the eligibility gate enforces the 3-attempt retry budget. For long-running cert workflows the answer is to re-validate, not to make this tool stateful. OUTCOMES: certification_status ∈ {confirmed_production_ready (badge mints), downgraded_to_emerging (cert review surfaced a missed production_blocker, tier capped at C/emerging), unavailable_provider_error (LLM call failed, retry within budget)}. Cert findings + summary + attempt history surfaced on the persisted run for full inspectability.
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  • Query the Trillboards API changelog for recent changes, breaking changes, deprecations, and fixes. WHEN TO USE: - Check what has changed in the API before upgrading an integration. - Find breaking changes since a specific date. - Discover new features added to a specific API surface. PARAMETERS: - since (YYYY-MM-DD, optional): Only entries dated on or after this date. Unreleased entries are always included. - type (string, optional): Filter by change category. Accepts: "breaking" → changed + removed entries "additive" → added entries "deprecation" → deprecated entries "fix" → fixed entries Can be comma-separated: "breaking,deprecation" RETURNS: - object: "list" - data: Array of { version, date, type, surface, description } - total: Number of matching entries. EXAMPLE: Agent: "What broke since April 1st?" query_changelog({ since: "2026-04-01", type: "breaking" })
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  • WRITE to the Knowledge Base. This tool has TWO modes: **MODE 1 — SAVE a new card**: Provide `content` with full Markdown following the ACTIONABLE schema below. **MODE 2 — REPORT OUTCOME**: Provide `kb_id` + `outcome` ('success' or 'failure'). WHEN TO USE: - Mode 1: After successfully fixing a bug IF no existing KB card covered it. - Mode 2: ALWAYS after applying a solution from `read_kb_doc` and running verification. INPUT: - `content`: (Mode 1) Full Markdown KB card content — follow the EXACT template below. - `overwrite`: (Mode 1) Set to True to update an existing card. - `kb_id`: (Mode 2) ID of the card to report outcome for. - `outcome`: (Mode 2) 'success' or 'failure'. - `enrichment`: (Mode 2, optional) Additional context to merge into the card when outcome is 'failure'. ━━━ CARD TEMPLATE (Mode 1) — copy this structure EXACTLY ━━━ ``` --- kb_id: "[PLATFORM]_[CATEGORY]_[NUMBER]" # e.g. WIN_TERM_001, CROSS_DOCKER_002 title: "[Short Title — max 5 words]" category: "[terminal|devops|supabase|fastmcp|network|database|...]" platform: "[windows|linux|macos|cross-platform]" technologies: [tech1, tech2] complexity: [1-10] criticality: "[low|medium|high|critical]" created: "[YYYY-MM-DD]" tags: [tag1, tag2, tag3] related_kb: [] --- # [Short Title — max 5 words] > **TL;DR**: [One sentence — what's the problem + solution] > **Fix Time**: ~[X min] | **Platform**: [Windows/Linux/macOS/All] --- ## 🔍 This Is Your Problem If: - [ ] [Symptom 1 — specific symptom or error message] - [ ] [Symptom 2 — specific error code or log line] - [ ] [Symptom 3 — environment/version condition] **Where to Check**: [console / logs / env / task manager / etc.] --- ## ✅ SOLUTION (copy-paste) ### 🎯 Integration Pattern: [Global Scope] / [Inside Init] / [Event Handler] ```[language] # [One-line comment — what this code does] [depersonalized code WITHOUT specific paths, use __VAR__ for things to replace] ``` ### ⚡ Critical (won't work without this): - ✓ **[Critical Point 1]** — [why it's essential] - ✓ **[Critical Point 2]** — [common mistake to avoid] ### 📌 Versions: - **Works**: [OS/library versions where confirmed working] - **Doesn't Work**: [OS/library versions where known broken] --- ## ✔️ Verification (<30 sec) ```bash [single command to verify the fix worked] ``` **Expected**: ✓ [Specific output or behavior that confirms success] **If it didn't work** → see Fallback below ⤵ --- ## 🔄 Fallback (if main solution failed) ### Option 1: [approach name] ```bash [command] ``` **When**: [condition to use this option] | **Risks**: [what might break] ### Option 2: [alternative approach] ```bash [command] ``` **When**: [condition] | **Risks**: [what might break] --- ## 💡 Context (optional) **Root Cause**: [1 sentence — why this problem occurs] **Side Effects**: [what might change after applying the fix] **Best Practice**: [how to avoid this in future — 1 point] **Anti-Pattern**: ✗ [what NOT to do — common mistake] --- **Applicable**: [OS, library versions, conditions] **Frequency**: [rare / common / very common] ``` ━━━ END OF TEMPLATE ━━━ RULES for ACTIONABLE cards: 1. Solution FIRST — after diagnosis, code immediately 2. Depersonalize — no names, project names, or absolute paths 3. Use `__VAR__` markers for anything the user must replace 4. One Verification command, result visible in <30 sec 5. Fallback — 1-2 options max, always include When/Risks 6. Context at End — WHY is optional reading for curious agents
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  • Evaluates typography elements against a principled accessibility rubric. COST: $0.05 USDC via x402 on Base-compatible EVM network per call. Goes beyond what axe/Lighthouse/WAVE can check — evaluates design judgment, not just numeric compliance. Catches issues like: - Contrast that passes WCAG 4.5:1 but fails visually due to thin font weight - Body text that meets minimum size requirements but is still too small for comfortable reading - Line heights that technically comply but impede readability for dyslexic users - Extended all-caps or italic text that passes all AA criteria but impairs reading - Text on gradient/image backgrounds where scanner sampling is unreliable - Heading sizes that are technically correct but visually indistinct from body Args: - elements: Array of 1–50 typography element objects with font/color properties - screen_name: Optional label for the evaluation report Each element requires: element_type, font_size, font_weight, line_height, color_hex, background_color_hex. Returns: Structured report with: - Per-element scores (0–100) - Specific issues with severity (critical/major/minor) - WCAG references and what automated tools miss - Concrete fix recommendations - Overall score and verdict (pass/needs_work/fail) - Top issues sorted by severity Example use: Extract text layer properties from Figma using get_design_context, pass the typography properties to this tool for evaluation before shipping.
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  • Record (or refresh) the sandbox test for one or more existing test suites — captures the request/response per step plus the outbound mocks (DB, downstream HTTP, etc.) against the dev's locally-running app, then links the captures onto the suite. Use this when the dev says "record", "rerecord", "re-record", "refresh the recordings", "capture mocks", or as the RECORD step in FROM-SCRATCH (after create_test_suite). This tool resolves the app (if only a hint is given), resolves ONE OR MORE suites to record (by exact ids OR case-insensitive name substring match), and delegates to a headless playbook. Output produces a RERECORD REPORT — it answers "did the sandbox test get created and linked successfully?". ╔═══ PRE-CHECK — DID YOU ARRIVE HERE FROM A FAILED REPLAY? ═══╗ This tool refreshes the CAPTURED BASELINE (mocks + recorded request/response per step). It does NOT modify the suite's authored assert array or response.body — those are the contract as defined when the suite was created/updated. If the contract changed and you re-record without updating the suite first, the new rerecord fires the suite's stale assertions against the live app, gate-1-fails on the same diff, and the suite comes back unlinked. Before calling THIS tool in response to a failed replay_sandbox_test or replay_test_suite, walk these checks: 1. Read failed_steps[].authored_assertions and authored_response_body in the most recent get_session_report (kind=sandbox_run / test_suite_run). The fields are inlined — no second tool call needed unless the report predates the inlined fields. 2. For each failing step: does any authored assertion pin the diverging value? (e.g. assert {path: "$.order.status", expected: "created order"} where the diff says "expected 'created order', got 'created'".) * YES → call update_test_suite FIRST to update that assertion + the response.body field, THEN call this tool. * NO → safe to call this tool directly; the captured baseline drifts but no authored assertion blocks the rerecord. 3. If you can't find authored_assertions in the report (older format) AND don't already know the suite's shape, call getTestSuite({app_id, suite_id, branch_id}) to inspect the assert array before deciding. Don't guess. REFUSE-RULE: if the dev confirms a contract change is intentional and the failing step has a pinned authored assertion on the diverging value, you MUST run update_test_suite before this tool. Calling record_sandbox_test FIRST in that case is the bug this pre-check exists to prevent — don't justify it as "let's just refresh the baseline first". The order is update → record → replay; never record → update. ╚═══════════════════════════════════════════════════════════════╝ ===== BEFORE CALLING — one-time setup ===== (a) APP_ID RESOLUTION (skip if app_id is already known): * Derive a likely app name from the cwd's basename (e.g. cwd=/home/dev/orderflow → "orderflow"). Lowercase it. * Call listApps({q: "<cwd-basename>"}) — the server does a case-insensitive server-side substring match, so you don't paginate the full tenant list (can be hundreds of apps on shared accounts). * Exactly one match → use its id. Multiple → list them and ASK the dev which one (a wrong app_id silently routes traffic + suite creates into the wrong app). Exception: if the compose file / repo layout unambiguously pins one candidate (e.g. compose has service "producer" and one candidate is "<folder>.producer" while others are unrelated siblings), you may pick it AND tell the dev up-front so they can correct. * Zero matches → ASK permission to create a new Keploy app with the derived name; on yes, call createApp({name, endpoint}) and use the returned id. * Alternatively pass app_name_hint to THIS tool and the server resolves it (same rules; multiple/zero → typed error). (b) KEPLOY BINARY VERIFICATION: * Bash: "keploy --version" (or "~/.keploy/bin/keploy --version"). If it exits non-zero the binary is missing. * If missing OR older than this MCP server was built against, install/upgrade: curl --silent -O -L https://keploy.io/ent/install.sh && source install.sh * Re-verify with "keploy --version"; fail loudly if still absent (tell the dev where keploy put the binary so they can add it to PATH). ===== DOCKER-COMPOSE NETWORK RULE (absolute) ===== Use the SAME compose file + service that was used in the validate-curl phase. Do NOT point keploy at a second "keploy-only" compose file — docker-compose isolates each file into its own project + network, so the app container spawned by keploy cannot reach the DB/Kafka containers that validate brought up (and the network-name collision blocks keploy from starting). Correct flow: (i) Validate phase: "docker compose up -d" (brings up app + deps on network <project>_default). (ii) Before calling record_sandbox_test, Bash: "docker compose stop <app_service> && docker compose rm -f <app_service>" — stop ONLY the app service; leave deps running so keploy's new app container can reach them on the existing network. (iii) Pass app_command = "docker compose up <app_service>" (same compose file, same project → same network). container_name = the actual name set by compose (e.g. "orderflow-producer", not "producer"). ===== RESOLUTION RULES (server-side, no guessing) ===== 1. App: caller provides app_id OR app_name_hint. With a hint, the server does listApps({q: hint}). Zero matches → typed error; multiple → typed error listing them so Claude asks the dev. 2. Suites: DEFAULT IS "ALL LINKED". When the dev says "record my sandbox tests" / "rerecord everything" / "refresh my recordings" with no specific suite named, LEAVE BOTH suite_ids AND suite_name_hint UNSET. Do NOT list suites first and pass a comma-joined UUID list back — the CLI resolves "every linked suite for the app" itself, cleaner and less brittle. Only pass a narrower selector when the dev explicitly names suites: - suite_ids (comma-separated, exact) — when you already have the IDs. - suite_name_hint (case-insensitive substring match) — when the dev names suites by human phrasing like "the auth suite" or "deterministic". Every suite whose name contains the substring is recorded. If the dev asks to record suites that don't exist yet (zero match) → typed error. Any ≥1 match is fine. DO NOT prompt the dev for which suites to record — default to all linked if they didn't name any. ===== DISCOVERY — when the dev hands you a bare suite_id with no app_id / branch_id ===== Suites live on a (app_id, branch_id) tuple. A bare suite_id has NO on-disk hint about which app or branch holds it; you have to RESOLVE both before calling this tool. Walk these steps in order — STOP as soon as getTestSuite returns 200: 1. Detect the dev's git branch: Bash `git rev-parse --abbrev-ref HEAD` in app_dir. If exit non-zero / output is "HEAD" → not a git repo / detached HEAD; ASK the dev for the Keploy branch name. 2. Resolve candidate apps via the cwd basename: Bash `basename $(pwd)` → call listApps with q=<basename>. Usually 1–2 candidates. If 0 → ASK; if >1 → walk every candidate in step 4. 3. For each candidate app, call list_branches({app_id}) and find the branch whose `name` matches the git branch from step 1. That gives you {branch_id}. If no match → not this app, try next. 4. Verify with getTestSuite({app_id, suite_id, branch_id=<from step 3>}). 200 → resolved; 404 → wrong app/branch, try next. 5. If steps 2–4 exhaust without a hit, walk every OPEN branch on each candidate app via list_branches → getTestSuite. Then try main (branch_id omitted). If still nothing → ASK the dev for the {app_id, branch_id} pair. The standard pattern when "search the suite by id" returns nothing is NOT "give up and ask the dev which app" — it's "the suite exists on a BRANCH, walk discovery". Suites created via create_test_suite + rerecord on a Keploy branch are INVISIBLE to a main-view listTestSuites; you have to scope each call to a branch. After resolving once in a session, REUSE the {app_id, branch_id} for any subsequent suite-targeted call (replay_sandbox_test, update_test_suite, replay_test_suite); don't re-walk discovery for every action. ===== PREREQUISITES ===== - app_command: shell command that starts the dev's app (e.g. "docker compose up producer"). - app_url: base URL the app listens on, e.g. http://localhost:8080. - app_dir: absolute path to repo root. - container_name if app_command is docker-compose. - keploy binary on PATH. If `which keploy` returns nothing, install it before calling this tool with: `curl --silent -O -L https://keploy.io/install.sh && source install.sh`. ===== AFTER CALLING — walk the playbook ===== The response includes a "playbook" array; execute its steps in order. The flow is HEADLESS — one background process, NDJSON progress events on a local file, no separate HTTP surface to bind. THERE IS NO SEPARATE CLEANUP STEP — the CLI exits on its own once phase=done is written. 1. Spawn the `keploy record sandbox --cloud-app-id …` process via Bash (run_in_background). Capture its PID into $KEPLOY_PID. 2. Poll progress by repeatedly calling Bash with `tail -n 1 $PROGRESS_FILE`. Each call returns instantly; the MCP round-trip between calls paces the loop. DO NOT wrap in a sleep loop — Claude Code's Bash rejects standalone `sleep N` and chained-sleep patterns. Read .phase off each line; stop when phase=done. The wait_for_done step's built-in `kill -0 $KEPLOY_PID` check is the safety-net for silent early-exit (CLI died before writing the terminal event) — it lets the loop exit instead of spinning forever on a dead process. 3. Read the terminal event (last line of $PROGRESS_FILE). It carries data.ok, data.error (on failure), data.test_run_id (on success). 4. On data.ok=true: call get_session_report(app_id, test_run_id) with verbose=true to surface the rerecord report. On data.ok=false: show data.error to the dev directly (optionally tail the log_file for stderr context) and SKIP get_session_report (there's no run to fetch). Auto-replay + linkTestSetToSuite run INSIDE the CLI process before it writes phase=done — if the terminal event says ok=true, linkage already happened. You do NOT need to wait for a separate post-success window; the CLI doesn't exit until it's fully done. INTERRUPTED FLOWS: if your conversation dies between step 1 and step 2 (Claude crashes, connection drops, dev cancels), the CLI keeps running in the background. It's not orphaned — it'll finish its run and write phase=done. To abort early, the dev can `pkill -f "keploy.*sandbox"` manually; otherwise just let it complete and resume by re-reading the progress file on the next turn. ===== NDJSON SCHEMA — the contract ===== Every line in the progress_file is one JSON object with this envelope: { "ts": "<RFC3339-nano>", "command": "record" | "test", "phase": "<phase-name>", "message": "<optional human-readable>", "data": { ... phase-specific ... } // optional } The phase vocabulary is intentionally extensible — new lifecycle phases get added over time as the CLI grows (started, agent_up, app_starting, suites_running_start, record_done, auto_replay_skipped, upload_done, linking_done, etc.). There are only TWO phases the AI must handle programmatically; everything else is informational and you should NOT switch on phase names you don't recognize: * phase != "done" → keep polling. Optional: surface message/data to the dev as ambient progress ("agent is starting...", "suites uploading..."), but never branch on a specific intermediate phase name. * phase == "done" → terminal event. Stop polling. The data envelope carries: - data.ok bool true on success, false on failure - data.error string (only on ok=false) one-line failure summary - data.test_run_id string (only on ok=true) pass to get_session_report - data.app_id string echo of the app_id passed to the tool - data.artifact_dir string local path to captured/replayed artifacts - data.dashboard_url string UI link to drill into the run If you observe a phase you don't recognize, IGNORE it and keep polling. If "done" itself is renamed by a future CLI version, the wait_for_done step's PID-alive guard is your safety net (the poll loop exits when the CLI dies); surface log_file contents to the dev. ===== "ALL SUITES FAILED CAPTURE" — special signal ===== If you see a `phase: "auto_replay_skipped"` event with `message: "all suites failed during rerecord; skipping replay + linking"` ahead of the terminal `done` event, every suite failed at the CAPTURE phase (before auto-replay even ran). The CLI fails closed in this case — auto-replay and suite linking are SKIPPED, so every per_suite entry comes back linked=false. Watch for this trap: the terminal `data.ok=true` because the CLI itself completed cleanly (it didn't crash; it just had nothing to record successfully). DO NOT read data.ok=true as "rerecord succeeded" — read `<linked>/<total>`. If linked == 0, this is a HARD failure that needs diagnosis, not a partial-linkage case. ALWAYS surface the dashboard URL on this case. The terminal `done` event still carries `data.dashboard_url` and `data.test_run_id` (atg's TestSuiteRun was created during the capture phase); emit them verbatim so the dev can drill into per-step failures in the UI: "0/N suites have a sandbox test — every suite failed during the capture phase, so auto-replay and linking were skipped. Dashboard: <data.dashboard_url> (test_run_id=<id>)" EDGE CASE: if `data.test_run_id` is empty, atg never inserted a TestSuiteRun (typically a pre-flight validation failure — branch-id rejection, app unreachable, etc.). The dashboard URL won't resolve. Skip the URL, surface the log_file contents instead so the dev can read the early-stage failure. Recovery is the same as WHEN linked=false below — read failed_steps for each suite and pick route B (fix code) / C (update suite + record again) / SKIP. Don't infra-retry; capture-phase failures across every suite usually mean the app is broken, the suite shapes are stale, or the dev's local app isn't reachable. ===== LINKAGE VERIFICATION ===== After get_session_report returns, for EVERY suite that went into this record, call getTestSuite({suite_id}) and check whether the suite has a sandbox test (linked=true / non-empty test_set_id). A suite without a sandbox test cannot be replayed — replay_sandbox_test will 400 on it with "no sandboxed tests" until a successful record produces one. ===== WHEN linked=false — recovery rules ===== A suite with linked=false after record_sandbox_test means the record process couldn't produce a sandbox test for that suite. The SUITE ITSELF still exists; it just has no sandbox test. Diagnose WHY by reading the rerecord report's failed_steps for that suite: * No failed_steps OR pure infra error (link-commit / upload failed, no step diverged) → call record_sandbox_test AGAIN scoped to just the unlinked suite_ids. The tool is idempotent on the suite; safe to re-run. * failed_steps with assertion diffs (response shape, body fields, status code shifted from what the suite expected) → the suite is stale relative to current app behavior. The CONTRACT changed: - Change is INTENTIONAL (new field, renamed key, different status code is the new normal) → call update_test_suite to update the affected step's response / assertions to match the new contract, THEN call record_sandbox_test on the updated suite. - Change is UNINTENTIONAL (app regressed) → fix the app code first, then call record_sandbox_test. No suite update needed; the original test was correct. * failed_steps with 500s / handler crashes / connection refused → the app is broken at the wire level. Fix the app, then call record_sandbox_test. Don't update_test_suite to absorb a real failure. NEVER: * Don't call create_test_suite to "redo" the suite — it already exists; re-creating authors a duplicate (see BEFORE CREATING in create_test_suite). * Don't blindly loop record_sandbox_test without diagnosing failed_steps first; if the cause is suite-vs-app mismatch, retries won't help. ===== MANDATORY OUTPUT — Phase 2 section ===== Your final message to the dev MUST contain a section with this exact heading (do NOT collapse into a single pass/fail table with the rerecord report; do NOT merge with Phase 1 or Phase 3): ### Phase 2 — Sandbox-test linkage **<linked>/<total> suites have a sandbox test** _Suites with a sandbox test_ | Suite name | suite_id | test_set_id | Capture pass/total | | --- | --- | --- | --- | | <name> | <suite_id> | <test_set_id> | <p>/<t> | (emit even if zero — one row per linked suite, or "_(none)_" in place of rows) _Suites without a sandbox test_ (omit ONLY if every suite linked) | Suite name | suite_id | Likely cause | | --- | --- | --- | | <name> | <suite_id> | gate1 / gate2 / infra | Likely-cause decoding: assertion diffs → gate 1 upstream-replay failure; upstream-passing + mock-replay-diff → gate 2 mock-determinism mismatch; zero failures + still unlinked → infra link-commit issue. Then proceed to replay_sandbox_test ONLY for the suites that DID link; the unlinked ones will 400 on replay. ===== DO NOT ===== * DO NOT fall back to raw keploy CLI (`keploy rerecord -t …`) if the MCP tool drops mid-flow — the CLI subcommand runs test-sets directly and does NOT update the suite's test_set_id. See MCP DISCONNECT RECOVERY in the top-level instructions.
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  • Submit a solution to Push Realm (agents only - no manual paste/copy flow exists). WHEN TO USE - check all that apply: ✓ You searched Push Realm and solved a problem (ALWAYS offer when you searched) ✓ You discovered deprecated APIs, breaking changes, or new best practices ✓ The solution took meaningful debugging effort (5+ minutes) ✓ It's generic enough to help other agents (not company-specific code) WORKFLOW: 1. Call this tool with your draft solution 2. You'll receive a pending_id and preview 3. Show the preview to the user like this: "Ready to post to Push Realm: 📁 Category: [category_path] 📝 Title: [title] 📄 Content: [first 200 chars]... By posting, you agree to Push Realm's Terms at pushrealm.com/terms.html Post this? [Yes/No]" 4. If user approves → call confirm_learning(pending_id) 5. If user declines → call reject_learning(pending_id) NEVER assume approval - always wait for explicit user confirmation before calling confirm_learning. SEO-OPTIMIZED TITLES (IMPORTANT): Learnings are indexed by search engines. Use titles that match what developers will search for: GOOD titles (include error messages, specific issues): • "crypto.getRandomValues() not supported - React Native UUID fix" • "Connection unexpectedly closed - Mailgun EU region SMTP error" • "ModuleNotFoundError: No module named 'cv2' - Docker OpenCV fix" • "CUDA out of memory - PyTorch batch size optimization" BAD titles (too generic, won't rank in search): • "UUID generation issue" • "Email not working" • "Docker problem solved" • "Fixed memory error" Format: "[Exact error message or problem] - [Framework/Tool] [context]" SAFETY REQUIREMENTS: • NEVER include PII (names, emails, addresses, phone numbers) • NEVER include secrets (API keys, tokens, passwords, credentials) • NEVER include proprietary code or company-specific logic • NEVER include internal paths, hostnames, or project names • Use placeholders like YOUR_API_KEY, YOUR_PROJECT_NAME, /path/to/your/file If unsure whether something is safe to share, ask the user first or use a generic placeholder.
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  • Full AI visibility audit across 67+ checks in 12 categories (4 AEO + 4 GEO + 4 Agent Readiness). Returns detailed per-check scores with specific issues and recommendations, AI Identity Card with mention readiness and detected competitors, and business profile. GEO checks include 3 research-backed citation signals: factual density, answer frontloading, and source citations. Agent Readiness covers emerging agent-discovery standards Cloudflare's isitagentready.com evaluates: RFC 9727 api-catalog, SEP-1649 MCP Server Card, and IETF Content-Signal (draft-romm-aipref). Does NOT generate fix code — use fix_site for that, or compare_sites to benchmark against a competitor. Pay per call ($1.00) via x402 — USDC on Base or Solana. Machine payment via signed X-PAYMENT header; see https://www.x402.org/. On payment_required, the response includes the full x402 payload with payTo/amount/asset.
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  • Resume work from a saved cognitive context. This provides a narrative briefing to quickly orient you to: - The investigation that was in progress - Key discoveries and insights made - Current hypotheses being tested - Open questions and blockers - Suggested next steps - All relevant memories with their connections The briefing reconstructs the cognitive state, not just the data. You'll understand not just WHAT was discovered, but WHY it matters and HOW the understanding evolved. Example of what you'll receive: "[API Timeout Investigation - Resuming after 2 hours] SITUATION: You were investigating production API timeouts that occur at exactly batch_size=100. This investigation started when user reported timeouts only in production, not staging. PROGRESS MADE: - Identified sharp cutoff at 100 items (not gradual degradation) - Disproved connection pool theory (monitoring showed only 43/200 connections used) - Found root cause: MAX_BATCH_SIZE=100 hardcoded in batch_handler.py:147 - Confirmed staging uses different config override (MAX_BATCH_SIZE=500) EVIDENCE CHAIN: User report → Reproduced locally → Noticed batch_size correlation → Searched codebase for limits → Found MAX_BATCH_SIZE → Checked staging config → Discovered config difference CORRECTED MISUNDERSTANDINGS: - Initially thought it was Redis connection exhaustion (disproven by monitoring) - Assumed gradual performance degradation (actually sharp cutoff) - Thought staging/production were identical (config differs) CURRENT HYPOTHESIS: Production deployment uses default MAX_BATCH_SIZE=100 from code, while staging has environment variable override. Fix requires either code change or prod config update. BLOCKED ON: Need production deployment access to apply fix. User considering whether to change code default or add production environment variable. RECOMMENDED NEXT STEPS: 1. Verify production environment variables (check if MAX_BATCH_SIZE is set) 2. If not set, add MAX_BATCH_SIZE=500 to production config 3. If code change preferred, update default in batch_handler.py 4. Run load test with batch_size=100-500 range to verify fix KEY MEMORIES FOR REFERENCE: - 'Initial timeout report from user' - Starting point of investigation - 'MAX_BATCH_SIZE discovery' - Root cause identification - 'Redis monitoring data' - Evidence disproving connection theory - 'Staging config analysis' - Explanation for environment difference" This cognitive handoff ensures you can continue the work with full understanding of the problem space, previous attempts, and current direction. The narrative preserves not just facts but the reasoning process, mistakes made, and lessons learned. SPECIAL CASE: restore_context("awakening") The name "awakening" is reserved for loading the user's personality configuration. This loads the Awakening Briefing which includes: - Selected persona identity and voice style - Custom personality traits (Premium+ users) - Any quirks and boundaries from the persona preset Args: name: Name or ID of context to restore. Can be: - Context name (exact match, case-sensitive) - Context UUID (from list_contexts output) - "awakening" for personality briefing limit: Maximum number of memories to restore (default 20) ctx: MCP context (automatically provided) Returns: Dict with: - success: Whether restoration succeeded - description: The cognitive handoff briefing - memories: List of relevant memories - context_id: The restored context identifier
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  • Tell the Hatchable team about a platform footgun, friction point, or surprising behavior you hit during this build. Reports go straight into the platform's triage queue and turn into bug fixes, doc updates, or explicit decisions. **When to call:** any time a platform constraint, undocumented limit, misleading error, missing helper, or stale doc cost you more than ~5 minutes to figure out — OR any time you successfully reach for a non-obvious workaround that future builds shouldn't have to rediscover. Calling mid-build (right after the workaround) is more useful than at the end of the build, because the painful details are still fresh. **Report quality matters:** the title should be one sentence ("TextDecoder caps decoded strings at 32 KB per decode() call"). The body should describe what you tried, the error you got, and the workaround. Reports become Github issues / docs PRs verbatim — write for the engineer who'll fix it, not for yourself. **Don't use this for:** generic praise, app-level bugs in the user's own code, anything that's already documented (search skills first via list_skills).
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  • Get full details for a specific electronic component by manufacturer part number (MPN) or LCSC number. Returns specs, pricing, and stock from all configured providers, plus the cached datasheet summary if available. Includes datasheet_status and available_sections when ready. Set prefetch_datasheet=true to trigger background extraction — no extra charge. Use after search_parts to drill into a specific result. The part_number must be a specific manufacturer part number (e.g. 'TPS54302DDCR', 'STM32F446RCT6') or LCSC number (e.g. 'C2837938'). Do NOT pass bare component values ('100nF', '10K'), descriptions ('buck converter'), or reference designators ('R1', 'U3'). DATASHEET STATUS VALUES: - 'ready' — extracted and indexed; call read_datasheet, search_datasheets, or analyze_image. - 'extracting' / 'in_progress' / 'queued' / 'pending' — extraction running or scheduled. Poll check_extraction_status every 5-10s until 'ready' or 'failed'. Typical time: 30s-2min. - 'not_extracted' — known part but datasheet hasn't been fetched yet. Trigger it via prefetch_datasheets (cheapest) or by calling read_datasheet (auto-triggers on first read). - 'no_source' — we couldn't find a public datasheet URL for this MPN. First, retry prefetch_datasheets in 10-30s (the URL resolver re-runs and often finds a source on the second pass). If still 'no_source', the agent can upload the PDF manually via request_datasheet_upload + confirm_datasheet_upload (see those tools). Org-uploaded datasheets are private to the org. - 'unsupported' — PDF exists but can't be extracted (scanned image-only, encrypted, or corrupted). Upload a clean text-based PDF via request_datasheet_upload to override. - 'failed' / 'error' — extraction errored. The response includes the error reason. Retry via prefetch_datasheets or escalate to support. - 'rejected' — input wasn't a real MPN (bare value like '100nF', description, or reference designator). Fix the input and re-call. - 'deduplicated' — another part in the family already has this datasheet; same content is returned under the primary MPN.
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  • Map error OR free-text query to a verified fix. USE WHEN: user pastes a concrete error/stack (ENOENT, ImportError, build failure) — pass `error`. OR user describes a symptom ('webpack slow', 'pip stuck') — pass `query`. Always prefer this over guessing a fix. RETURNS: exact-match {status, solution, confidence, source_url} or search results [{title, summary, source_url}].
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