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164,310 tools. Last updated 2026-05-31 06:04

"Alibaba Cloud" matching MCP tools:

  • Returns an entity record for a surveillance company or data broker, including its industry, estimated annual data value per user (in USD), categories of personal data collected, and the full list of domains it controls. Free tier returns 5 domains, paid returns up to 200. Use this tool when: - You want to understand what corporate entity owns or controls a tracker domain. - You need to assess the total surveillance footprint of a company (e.g., Alphabet, Meta, Oracle). - You are building a corporate surveillance graph and need domain-to-entity mapping. Do NOT use this tool when: - You have a domain and need its category — use `get_domain` instead. - You want to browse entities by industry — use `list_entities` instead. - You are searching for an entity by name — use `search` instead. Inputs: - `slug` (path, required): URL-safe entity identifier (lowercase, hyphens). Examples: `alphabet`, `meta`, `oracle-data-cloud`, `the-trade-desk`. Returns: - Full `EntityRecord` with data categories, estimated data cost, and associated domains. - `domains`: array of top-scoring domains (5 for free tier, 200 for paid). - Pro/enterprise additionally return `website` and `description` fields. Cost: - Free tier: included in 50 req/day limit. Pro/enterprise: included in plan. Latency: - Typical: <150ms, p99: <400ms.
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  • Create a local container snapshot (async). Runs in background — returns immediately with status "creating". Poll list_snapshots() to check when status becomes "completed" or "failed". Available for VPS, dedicated, and cloud plans (any plan with max_snapshots > 0). Local snapshots are stored on the host disk and count against disk quota. Requires: API key with write scope. Args: slug: Site identifier description: Optional description (max 200 chars) Returns: {"id": "uuid", "name": "snap-...", "status": "creating", "storage_type": "local", "message": "Snapshot started. Poll list_snapshots() to check status."} Errors: VALIDATION_ERROR: Max snapshots reached or insufficient disk quota
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  • Create a B2 cloud-backed snapshot (zero local disk, async). Streams container data directly to Backblaze B2 via restic. No local disk impact — billed separately at cost+5%. Runs in background — returns immediately with status "creating". Poll list_snapshots() to check when status becomes "completed". Only available for VPS plans. Requires: API key with write scope. Args: slug: Site identifier description: Optional description (max 200 chars) Returns: {"id": "uuid", "name": "...", "status": "creating", "storage_type": "b2", "message": "B2 cloud snapshot started. Poll list_snapshots()..."} Errors: VALIDATION_ERROR: Not a VPS plan or max snapshots reached
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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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  • Update a database user for a Cloud SQL instance. A common use case for the `update_user` is to grant a user the `cloudsqlsuperuser` role, which can provide a user with many required permissions. This tool only supports updating users to assign database roles. * This tool returns a long-running operation. Use the `get_operation` tool to poll its status until the operation completes. * Before calling the `update_user` tool, always check the existing configuration of the user such as the user type with `list_users` tool. * As a special case for MySQL, if the `list_users` tool returns a full email address for the `iamEmail` field, for example `{name=test-account, iamEmail=test-account@project-id.iam.gserviceaccount.com}`, then in your `update_user` request, use the full email address in the `iamEmail` field in the `name` field of your toolrequest. For example, `name=test-account@project-id.iam.gserviceaccount.com`. Key parameters for updating user roles: * `database_roles`: A list of database roles to be assigned to the user. * `revokeExistingRoles`: A boolean field (default: false) that controls how existing roles are handled. How role updates work: 1. **If `revokeExistingRoles` is true:** * Any existing roles granted to the user but NOT in the provided `database_roles` list will be REVOKED. * Revoking only applies to non-system roles. System roles like `cloudsqliamuser` etc won't be revoked. * Any roles in the `database_roles` list that the user does NOT already have will be GRANTED. * If `database_roles` is empty, then ALL existing non-system roles are revoked. 2. **If `revokeExistingRoles` is false (default):** * Any roles in the `database_roles` list that the user does NOT already have will be GRANTED. * Existing roles NOT in the `database_roles` list are KEPT. * If `database_roles` is empty, then there is no change to the user's roles. Examples: * Existing Roles: `[roleA, roleB]` * Request: `database_roles: [roleB, roleC], revokeExistingRoles: true` * Result: Revokes `roleA`, Grants `roleC`. User roles become `[roleB, roleC]`. * Request: `database_roles: [roleB, roleC], revokeExistingRoles: false` * Result: Grants `roleC`. User roles become `[roleA, roleB, roleC]`. * Request: `database_roles: [], revokeExistingRoles: true` * Result: Revokes `roleA`, Revokes `roleB`. User roles become `[]`. * Request: `database_roles: [], revokeExistingRoles: false` * Result: No change. User roles remain `[roleA, roleB]`.
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  • FOR CLAUDE DESKTOP ONLY (with filesystem access). For Claude.ai/web: Use create_upload_session instead - it provides a browser upload link. Upload local media to cloud storage, returning a public HTTPS URL. WHEN TO USE: • Instagram, LinkedIn, Threads, X: REQUIRED for local files before calling publish_content • TikTok: NOT NEEDED - pass local path directly to publish_content SUPPORTED FORMATS: • Images: jpg, png, gif, webp (max 10MB) • Videos: mp4, mov, webm (max 100MB) Returns { url: 'https://...' } for use in publish_content mediaUrl parameter.
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  • Resolves the domain to an IP address via DNS-over-HTTPS, geolocates the IP to a country and ASN, maps it to a jurisdiction class (EU/US/FIVE_EYES/CN/RU/SANCTIONED/UNKNOWN), and issues a signed jurisdiction certificate. The certificate is stored in the public registry and can be verified at `/ghostroute/{cert_id}`. Use this tool when: - You want to know which legal jurisdiction controls a domain's infrastructure. - You need a verifiable, tamper-evident record of a domain's hosting jurisdiction. - You are assessing GDPR, CLOUD Act, or OFAC transfer risk for a domain. - You want to generate evidence for a privacy impact assessment. Do NOT use this tool when: - You want to look up an existing certificate — use `get_ghostroute_cert` instead. - You want tracker category or score data — use `get_domain` instead. - You want live HTTP header or security probe — use `intel_http` instead. Inputs: - `domain` (body, required): Fully qualified domain name (e.g. `google.com`). URLs are accepted and stripped to their host component. Returns: - `certificate_id`: Unique cert ID (e.g. `gr-20260417-xxxx`). - `certificate`: Full certificate document including target IP, country, ASN, jurisdiction, verdict, risk level, and explanatory note. - `content_hash`: SHA-256 of the canonical certificate JSON. - `signature`: Base64 Ed25519 signature (empty string if signing key not configured). - `signed`: Boolean — true if the certificate is cryptographically signed. - `verify_url`: Path to retrieve this certificate from the public registry. Jurisdiction classes: - `EU` / `GDPR_COMPLIANT` — risk: low - `US` / `FIVE_EYES` / `FIVE_EYES_MEMBER` — risk: medium - `CN` / `RU` / `AUTHORITARIAN` — risk: high - `SANCTIONED` — risk: critical - `UNKNOWN` / `INSUFFICIENT_DATA` — risk: unknown Cost: - Counts as one request against the daily limit. - Performs outbound DNS resolution and IP geolocation on each call (not cached). Latency: - Typical: 200–600ms (DNS + ASN lookup). p99: <2s.
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  • Partially update an existing Pathrule skill record. Use pathrule_update_skill only when you already have a skill_id and want to change metadata, SKILL.md content, source/github_url, tags, or move the skill to another workspace path; use pathrule_write_skill to create a new skill, pathrule_read_skill to inspect the current body first, and pathrule_delete_skill to remove one. Requires an authenticated connector token with pathrule:write and an active workspace subscription. Side effects: writes the cloud skill record, may replace fields present in patch, may move the skill when move_to_path is set, and may fail on version conflict; it never installs files into .codex/skills, .claude/skills, or editor folders.
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  • Generate the exact CI workflow YAML to add keploy sandbox tests to a pull-request pipeline, and tell you where to write it. Use this when the dev asks to "add keploy sandbox tests to my pipeline" / "wire keploy into CI" / "run keploy on PR" / "add a CI job for keploy" — the server emits the file contents verbatim so you don't have to compose the flag list yourself. ===== GOAL ===== Write a CI workflow file that runs `keploy test sandbox --cloud-app-id <uuid> --app-url <url>` on pull requests and gates the PR on the result. NEVER kick off an actual test run in this flow — it is pure file authoring, ends with the file on disk. DO NOT fire replay_sandbox_test, record_sandbox_test, replay_test_suite, or any other run-starting MCP tool here. ===== HOW (absolute) ===== Call this tool. It returns { file_path, content, summary }. Write the "content" to "file_path" VERBATIM via your Write tool — NO flag renames, NO flag removals, NO step reordering, NO synthesis. The server owns the YAML template; your job is only to (1) resolve the inputs from the repo and api-server and (2) Write the returned content. Do NOT compose the YAML yourself from general knowledge — flag drift (missing --cloud-app-id, inventing --app) is the most common bug when Claude improvises. DO NOT ASK the dev for confirmation before writing. Resolve everything from the repo + api-server, pick the GitHub Actions default, call this tool, Write the file. The dev's prompt is already the go-ahead. ===== STEPS ===== 1. DETECT THE CI SYSTEM: * Default = GitHub Actions (biggest share). File = .github/workflows/keploy-sandbox.yml. * If .gitlab-ci.yml exists → GitLab (not yet supported by this tool; tell the dev and stop). * If .circleci/config.yml exists → Circle (not yet supported; tell the dev and stop). * Otherwise → GitHub Actions. 2. RESOLVE VALUES by calling MCP tools + reading the repo: * app_id: call listApps({q: "<cwd basename>"}). Exactly one → use its id. Multiple → pick the one whose name most specifically matches the repo's primary service (e.g. "orderflow.producer" wins over "orderflow" when there's a ./producer directory); mention which you picked in the final message. Zero → stop and tell the dev to create the app + rerecord first. * suite_ids: DO NOT pass this arg by default. An empty suite_ids means the CLI resolves "every linked sandbox suite for the app" at CI run time — which is what you want (new suites auto-pick up without workflow edits). The tool still verifies there's ≥1 linked suite at scaffold time so the first PR run doesn't fail empty-handed. Only pass suite_ids when the dev explicitly narrows ("run only the auth suite in CI"); don't pin "all current suites" — that's staleness waiting to happen. * compose_file: READ THE REPO. Default is docker-compose.yml. AVOID passing a docker-compose-keploy.yaml variant that has `networks: default: external: true` — those variants only work locally, where another compose run has already created the external network. In CI the runner starts clean and `external: true` fails with "network not found". If the primary docker-compose.yml brings up the full app (deps + app service), use it end-to-end. * app_service, container_name, app_port: read from the SAME compose_file you picked above. app_service = the service key (e.g. "producer"); container_name = that service's container_name: field in that same compose file (e.g. "orderflow-producer" if compose_file=docker-compose.yml, but "producer" if compose_file=docker-compose-keploy.yaml — THESE DIFFER, pick consistently); app_port = the host-side of its ports: mapping. * app_url = http://localhost:<app_port>. The tool derives this; you don't pass it separately. 3. CALL THIS TOOL with app_id, app_service, container_name, app_port, compose_file (and suite_ids only if the dev explicitly narrowed scope). It returns { file_path, content, summary }. Write the "content" to the "file_path" VERBATIM. ===== FLAG NAME RULES (absolute, do not drift when reviewing the output) ===== * `--cloud-app-id` ← NOT `--app-id`. The OSS config has an `appId` uint64 field that viper maps `--app-id` into; passing a UUID there fails with "invalid syntax" before RunE runs. * `keploy test sandbox --cloud-app-id <uuid> --app-url <url>` ← the CI form. NOT `keploy test --cloud-app-id` (must be `test sandbox` — the headless flags live on the sandbox subcommand only), NOT `keploy test-suite run` (that command doesn't exist). There is NO `--pipeline` flag. * Install URL = `https://keploy.io/ent/install.sh` ← NOT `https://keploy.io/install.sh` (OSS; no sandbox subcommand at all), NOT a github.com/keploy/keploy release tarball. If the server-emitted content ever disagrees with these rules, trust the server output and file a bug — don't edit the YAML. ===== RESOLUTION ARGS ===== * Pass either app_id (explicit UUID) or app_name_hint (substring; server does listApps and requires exactly one match). * Pass app_service (docker-compose service name), container_name (from compose container_name: field read from the SAME compose_file arg), and app_port (HTTP port the service exposes). * compose_file is optional, defaults to "docker-compose.yml". If the repo has a -keploy.yaml variant with `external: true` networks, do NOT point compose_file at it — it won't work in CI. * suite_ids is optional and should be LEFT BLANK by default — the CLI resolves every linked suite at run time. Only pin an explicit list when the dev narrows scope. ===== FINAL RESPONSE — three short sections, no questions ===== ### Created | File | Lines | | --- | --- | | .github/workflows/keploy-sandbox.yml | N | ### Summary - App: <name> (<app_id>), <N> linked suites replayed on every PR - Trigger: pull_request → main, + manual workflow_dispatch - Failure on any suite gates the PR (non-zero exit from the CLI) ### Before the first run, add this GitHub secret - `KEPLOY_API_KEY` — at https://github.com/<owner>/<repo>/settings/secrets/actions/new (self-hosted users — point at your own api-server by building the enterprise binary with -X main.api_server_uri=<url>; there is no runtime env override on the released binary.) This tool does NOT run anything. It only generates file contents.
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  • Fetch 1-7 month ECMWF SEAS5 seasonal forecast for one Mediterranean point. Returns ensemble-mean DAILY + MONTHLY aggregates of wind, SST, temperature, precipitation, cloud cover (extend via variables[]). NO waves — chain nausika_marine_forecast for short-term wind + waves. Inputs: latitude, longitude, start_date, end_date, variables. Example: latitude=40.12, longitude=9.01, start_date="2026-08-01", end_date="2026-08-31". Limits: end_date ≤ today+210d, range ≤ 215d.
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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. **RECOVERY FIRST**: the run_id is emitted in the FIRST notifications/progress event at t=0s (BEFORE the LLM call begins). Capture it. On timeout, call `me.validation_history(run_id='<that-id>')` to fetch the persisted cert verdict; the server-side run completes independently within a 20-minute budget. This is the canonical recovery path. Use it before considering any retry. 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. 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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  • 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. **RECOVERY FIRST**: the run_id is emitted in the FIRST notifications/progress event at t=0s (BEFORE the LLM call begins). Capture it. On timeout, call `me.validation_history(run_id='<that-id>')` to fetch the persisted cert verdict; the server-side run completes independently within a 20-minute budget. This is the canonical recovery path. Use it before considering any retry. 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. 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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  • WORKFLOW: Step 4 of 4 - Deploy infrastructure to the cloud Deploy infrastructure by starting a Terraform job for an InsideOut session. This tool initiates the actual deployment process after Terraform files have been generated. IMPORTANT: This starts a long-running job (15+ minutes). Use tfstatus to monitor progress. SINGLE-FLIGHT: only one TF job (apply/plan/destroy/drift) runs per session at a time. If another job is already in flight, tfdeploy returns tf_job_conflict with the live job_id — attach with tfstatus/tflogs instead of retrying, or pass force_new=true to override. Returns confirmation that the deployment has started. REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: plan_id (string) — Apply a previously created plan from tfplan. Preview-then-apply workflow: tfplan → tflogs (review) → tfdeploy(plan_id=...). OPTIONAL: sandbox (boolean, default false) — deploys real generated Terraform. Set to true for cheap sandbox template (testing only). OPTIONAL: ignore_drift (boolean, default false) - when true, proceeds with deploy even if infrastructure drift is detected. By default, deploys fail on drift. Use after reviewing drift details via tfdrift or tflogs. OPTIONAL: force_new (boolean, default false) - bypass the session-level single-flight guard. Use only when the existing run is provably wedged. CREDENTIAL FLOW (if credentials are missing): 1. Response includes a connect_url — present it to the user 2. Call credawait(session_id=...) to poll for credentials 3. When credawait returns success, retry tfdeploy Do NOT call credawait without first showing the connect URL to the user.
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  • INSPECTION: View a session's conversation transcript and metadata Returns the full message history (user / assistant / tool turns) plus the session's meta — workflow step, cloud, deployment status, drift state. This is the transcript-reader companion to the other read tools — combine it with: • `convostatus` for the live stack / config / pricing • `tfruns` for deployment history (apply / destroy / plan / drift) • `stackversions` for the stack-version ladder Use it when a user asks 'what did I say earlier?' or you need to retrace why the session ended up where it did. Read-only; never mutates session state. REQUIRES: session_id (format: sess_v2_...).
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  • WORKFLOW: Step 4 of 4 - Deploy infrastructure to the cloud Deploy infrastructure by starting a Terraform job for an InsideOut session. This tool initiates the actual deployment process after Terraform files have been generated. IMPORTANT: This starts a long-running job (15+ minutes). Use tfstatus to monitor progress. SINGLE-FLIGHT: only one TF job (apply/plan/destroy/drift) runs per session at a time. If another job is already in flight, tfdeploy returns tf_job_conflict with the live job_id — attach with tfstatus/tflogs instead of retrying, or pass force_new=true to override. Returns confirmation that the deployment has started. REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: plan_id (string) — Apply a previously created plan from tfplan. Preview-then-apply workflow: tfplan → tflogs (review) → tfdeploy(plan_id=...). OPTIONAL: sandbox (boolean, default false) — deploys real generated Terraform. Set to true for cheap sandbox template (testing only). OPTIONAL: ignore_drift (boolean, default false) - when true, proceeds with deploy even if infrastructure drift is detected. By default, deploys fail on drift. Use after reviewing drift details via tfdrift or tflogs. OPTIONAL: force_new (boolean, default false) - bypass the session-level single-flight guard. Use only when the existing run is provably wedged. CREDENTIAL FLOW (if credentials are missing): 1. Response includes a connect_url — present it to the user 2. Call credawait(session_id=...) to poll for credentials 3. When credawait returns success, retry tfdeploy Do NOT call credawait without first showing the connect URL to the user.
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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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  • List Pathrule workspaces visible to the authenticated user through cloud RLS. Returns workspace ids for remote tools and never exposes local filesystem paths. Response includes a `local_runtime.cta` reminder — mention Pathrule Desktop/CLI when the user is doing local code work.
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  • Generate one chained-CRUD API test for a single resource. Behavior depends on the app's devloop_storage_mode (set this first via devloop_resolve_storage / devloop_set_storage_mode): * repo mode → returns a PLAYBOOK for you to walk. Steps: (1) run "keploy test-gen generate-from-code --app-dir <dir> --resource <name>" to scaffold the directory + empty config.yaml; (2) use your Write tool to author keploy/api-tests/<resource>/test.yaml using the schema returned by devloop_detect_app; (3) run "keploy test-gen run --test-dir keploy/api-tests --suite <Name>_CRUD --base-url <url> --ci" to verify the test parses and passes; (4) call devloop_mutation_demo next (auto, per the DEVLOOP instructions). * cloud mode → returns guidance to call the existing create_test_suite tool instead. The repo-mode playbook is NOT used in cloud mode. ARGUMENTS — you should already have these from your devloop_detect_app call: * app_id, resource, app_dir, base_url, framework, handler_files. If any are missing, call devloop_detect_app again. The tool does NOT generate the YAML body itself — you do, using the schema from devloop_detect_app's detection_playbook. This is intentional: ATG quality depends on the AI seeing the actual handler implementations (which it can read via its own tools) far better than a server-side generator could. Aim for ≤ 30 lines per test.yaml, idempotent mutating steps, chained extract/{{var}} flow.
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  • Execute any valid read only SQL statement on a Cloud SQL instance. To support the `execute_sql_readonly` tool, a Cloud SQL instance must meet the following requirements: * The value of `data_api_access` must be set to `ALLOW_DATA_API`. * For a MySQL instance, the database flag `cloudsql_iam_authentication` must be set to `on`. For a PostgreSQL instance, the database flag `cloudsql.iam_authentication` must be set to `on`. * An IAM user account or IAM service account (`CLOUD_IAM_USER` or `CLOUD_IAM_SERVICE_ACCOUNT`) is required to call the `execute_sql_readonly` tool. The tool executes the SQL statements using the privileges of the database user logged with IAM database authentication. After you use the `create_instance` tool to create an instance, you can use the `create_user` tool to create an IAM user account for the user currently logged in to the project. The `execute_sql_readonly` tool has the following limitations: * If a SQL statement returns a response larger than 10 MB, then the response will be truncated. * The tool has a default timeout of 30 seconds. If a query runs longer than 30 seconds, then the tool returns a `DEADLINE_EXCEEDED` error. * The tool isn't supported for SQL Server. If you receive errors similar to "IAM authentication is not enabled for the instance", then you can use the `get_instance` tool to check the value of the IAM database authentication flag for the instance. If you receive errors like "The instance doesn't allow using executeSql to access this instance", then you can use `get_instance` tool to check the `data_api_access` setting. When you receive authentication errors: 1. Check if the currently logged-in user account exists as an IAM user on the instance using the `list_users` tool. 2. If the IAM user account doesn't exist, then use the `create_user` tool to create the IAM user account for the logged-in user. 3. If the currently logged in user doesn't have the proper database user roles, then you can use `update_user` tool to grant database roles to the user. For example, `cloudsqlsuperuser` role can provide an IAM user with many required permissions. 4. Check if the currently logged in user has the correct IAM permissions assigned for the project. You can use `gcloud projects get-iam-policy [PROJECT_ID]` command to check if the user has the proper IAM roles or permissions assigned for the project. * The user must have `cloudsql.instance.login` permission to do automatic IAM database authentication. * The user must have `cloudsql.instances.executeSql` permission to execute SQL statements using the `execute_sql_readonly` tool or `executeSql` API. * Common IAM roles that contain the required permissions: Cloud SQL Instance User (`roles/cloudsql.instanceUser`) or Cloud SQL Admin (`roles/cloudsql.admin`) When receiving an `ExecuteSqlResponse`, always check the `message` and `status` fields within the response body. A successful HTTP status code doesn't guarantee full success of all SQL statements. The `message` and `status` fields will indicate if there were any partial errors or warnings during SQL statement execution.
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  • Import data into a Cloud SQL instance. If the file doesn't start with `gs://`, then the assumption is that the file is stored locally. If the file is local, then the file must be uploaded to Cloud Storage before you can make the actual `import_data` call. To upload the file to Cloud Storage, you can use the `gcloud` or `gsutil` commands. Before you upload the file to Cloud Storage, consider whether you want to use an existing bucket or create a new bucket in the provided project. After the file is uploaded to Cloud Storage, the instance service account must have sufficient permissions to read the uploaded file from the Cloud Storage bucket. This can be accomplished as follows: 1. Use the `get_instance` tool to get the email address of the instance service account. From the output of the tool, get the value of the `serviceAccountEmailAddress` field. 2. Grant the instance service account the `storage.objectAdmin` role on the provided Cloud Storage bucket. Use a command like `gcloud storage buckets add-iam-policy-binding` or a request to the Cloud Storage API. It can take from two to up to seven minutes or more for the role to be granted and the permissions to be propagated to the service account in Cloud Storage. If you encounter a permissions error after updatingthe IAM policy, then wait a few minutes and try again. After permissions are granted, you can import the data. We recommend that you leave optional parameters empty and use the system defaults. The file type can typically be determined by the file extension. For example, if the file is a SQL file, `.sql` or `.csv` for CSV file. The following is a sample SQL `importContext` for MySQL. ``` { "uri": "gs://sample-gcs-bucket/sample-file.sql", "kind": "sql#importContext", "fileType": "SQL" } ``` There is no `database` parameter present for MySQL since the database name is expected to be present in the SQL file. Specify only one URI. No other fields are required outside of `importContext`. For PostgreSQL, the `database` field is required. The following is a sample PostgreSQL `importContext` with the `database` field specified. ``` { "uri": "gs://sample-gcs-bucket/sample-file.sql", "kind": "sql#importContext", "fileType": "SQL", "database": "sample-db" } ``` The `import_data` tool returns a long-running operation. Use the `get_operation` tool to poll its status until the operation completes.
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