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184,844 tools. Last updated 2026-06-08 20:05
"How to run Docker and view logs" matching MCP tools:
- MONITORING: Fetch Terraform deployment logs with pagination Fetches logs from a running or completed Terraform deployment job. For **completed jobs**: uses REST endpoint for instant retrieval (supports `tail` for server-side filtering). For **running jobs**: streams via SSE with timeout-based pagination. **PAGINATION** (running jobs only): Use `last_event_id` from the response to fetch more: 1. First call: `tflogs(session_id='...')` → get logs + `last_event_id` 2. Next call: `tflogs(session_id='...', last_event_id='...')` → get NEW logs only 3. Repeat until `complete: true` in response **RESPONSE FIELDS**: - `logs`: Array of log messages collected - `last_event_id`: Pass this back to get more logs (pagination cursor, SSE only) - `complete`: true if job finished, false if more logs may be available - `total_logs`: total log entries before tail truncation REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id to target a specific deployment (use tfruns to discover IDs), timeout (default 50s, max 55s), last_event_id (for pagination), tail (return only last N entries) ⚠️ CONTEXT WARNING: Deploy logs can be hundreds of lines. Use tail: 50 for completed jobs to avoid blowing up the context window.Connector
- DESTRUCTIVE: Permanently delete an app, its Docker service, volume, and all data including version history. This cannot be undone. You MUST confirm with the user before calling this tool.Connector
- 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.Connector
- MONITORING: Quick status check for Terraform deployments Check the current status of a Terraform deployment job. Use this tool to quickly check if a deployment is running, completed, or failed. Returns job status, job_id, and other metadata without streaming logs. Use tflogs to stream the actual deployment logs. REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id to target a specific deployment (use tfruns to discover IDs). **LIVENESS**: The response carries two distinct timestamps: - `updated_at` — last semantic change (only bumped when status / drift / version actually differ). Useful for sorting deployments; NOT a per-poll heartbeat. - `last_refresh_at` — last successful Oracle decode (stamped on every poll where reliable reached Oracle, even if nothing in the row changed). Use this to confirm reliable is still actively talking to Oracle for a long-running RUNNING job. Absent on rows that haven't been refreshed since the column was added. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.Connector
- Estimate credit cost for a conversion BEFORE running it. Returns word count, page calculation (300 words/page), and a credit breakdown by format and template type. Use this when the user asks 'how much will this cost?' or when you suspect a conversion might exceed their balance — convert_document refuses to run if credits are insufficient, so estimating first is friendlier.Connector
Matching MCP Servers
- AlicenseAqualityCmaintenanceEnables LLMs to explore and analyze UK Government BEIS inspect_ai evaluation logs directly from tools like Claude Code and Cursor. It provides capabilities to list logs, view evaluation summaries, and inspect conversation histories for specific samples.Last updated6MIT
- AlicenseAqualityCmaintenanceMCP server for GitHub Actions — view workflow runs, read logs, re-run failed jobs, and manage CI/CD.Last updated9283MIT
Matching MCP Connectors
Deploy applications to Cloud Run
Security audit for docker-compose.yml — 25 checks: secrets, privileges, network, volumes, images.
- MONITORING: Fetch Terraform deployment logs with pagination Fetches logs from a running or completed Terraform deployment job. For **completed jobs**: uses REST endpoint for instant retrieval (supports `tail` for server-side filtering). For **running jobs**: streams via SSE with timeout-based pagination. **PAGINATION** (running jobs only): Use `last_event_id` from the response to fetch more: 1. First call: `tflogs(session_id='...')` → get logs + `last_event_id` 2. Next call: `tflogs(session_id='...', last_event_id='...')` → get NEW logs only 3. Repeat until `complete: true` in response **RESPONSE FIELDS**: - `logs`: Array of log messages collected - `last_event_id`: Pass this back to get more logs (pagination cursor, SSE only) - `complete`: true if job finished, false if more logs may be available - `total_logs`: total log entries before tail truncation REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id to target a specific deployment (use tfruns to discover IDs), timeout (default 50s, max 55s), last_event_id (for pagination), tail (return only last N entries) ⚠️ CONTEXT WARNING: Deploy logs can be hundreds of lines. Use tail: 50 for completed jobs to avoid blowing up the context window.Connector
- Estimate credit cost for a conversion BEFORE running it. Returns word count, page calculation (300 words/page), and a credit breakdown by format and template type. Use this when the user asks 'how much will this cost?' or when you suspect a conversion might exceed their balance — convert_document refuses to run if credits are insufficient, so estimating first is friendlier.Connector
- Fetch the full results of a completed Disco run. Returns discovered patterns (with conditions, p-values, novelty scores, citations), feature importance scores, a summary with key insights, column statistics, and suggestions for what to explore next. The response includes a `dashboard_urls` object with direct links to each page of the interactive report — use these to direct the user to the most relevant view: - **summary**: AI-generated overview with key insights, novel findings, and plain-language explanation of the most important findings - **patterns**: Full list of discovered patterns with conditions, effect sizes, p-values, novelty scores, citations, and interactive visualisations - **features**: Feature importances, feature statistics and distribution plots, and correlation matrix - **territory**: Interactive 3D map showing how patterns select different regions of the data Only call this after discovery_status returns "completed". Args: run_id: The run ID returned by discovery_analyze. api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.Connector
- MONITORING: Quick status check for Terraform deployments Check the current status of a Terraform deployment job. Use this tool to quickly check if a deployment is running, completed, or failed. Returns job status, job_id, and other metadata without streaming logs. Use tflogs to stream the actual deployment logs. REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id to target a specific deployment (use tfruns to discover IDs). **LIVENESS**: The response carries two distinct timestamps: - `updated_at` — last semantic change (only bumped when status / drift / version actually differ). Useful for sorting deployments; NOT a per-poll heartbeat. - `last_refresh_at` — last successful Oracle decode (stamped on every poll where reliable reached Oracle, even if nothing in the row changed). Use this to confirm reliable is still actively talking to Oracle for a long-running RUNNING job. Absent on rows that haven't been refreshed since the column was added. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.Connector
- Retry a failed simulation run. Resets an errored run back to 'created' status and triggers a new package build. The same run ID is reused. Only valid when status is 'error'. Returns 409 for any other state.Connector
- Returns the calling account's id/email/role plus internal-use eligibility: whether the account is staff-flagged, which domains run free, and how a given target URL would be billed if you submitted a test now. Use this first when you bring TMV into a new project — it confirms the project's API key actually maps to the expected operator account.Connector
- Creates a materialized view or stored procedure in the project's BigQuery data warehouse for data pre-aggregation. **When to use this tool:** - When the user needs to pre-aggregate data from multiple connectors (e.g., cross-channel marketing report) - When a query is too slow to run on-demand and benefits from materialization - When the user asks to "create a view", "save this as a table", "materialize this query" **Naming rules (enforced):** - Target dataset MUST be 'quanti_agg' (created automatically if it doesn't exist) - Object name MUST start with 'llm_' prefix (e.g., llm_weekly_spend) - Format: CREATE MATERIALIZED VIEW quanti_agg.llm_name AS SELECT ... **SQL format:** - CREATE MATERIALIZED VIEW: for pre-computed aggregation tables - CREATE OR REPLACE MATERIALIZED VIEW: to update an existing view - CREATE PROCEDURE: for complex multi-step transformations **Example:** CREATE MATERIALIZED VIEW quanti_agg.llm_weekly_channel_spend AS SELECT DATE_TRUNC(date, WEEK) as week, channel, SUM(spend) as total_spend FROM prod_google_ads_v2.campaign_stats GROUP BY 1, 2 **Limits:** Maximum 20 active aggregation views per project.Connector
- Request an informational introduction — to TESSA itself, or to any directory firm if you pass target_firm_slug. TESSA logs the lead and either notifies sales@tessa.tech + kevincallen@tessa.tech (TESSA leads) or forwards a warm intro email to the firm with TESSA Cc'd (directory leads). No calendar booking — use request_strategy_session to book a meeting with TESSA.Connector
- Explain the Guard product using CurrencyGuard's approved product and FAQ content. Covers: what the Guard is, how it works, who it is for, how it compares to forwards or options, and legal, regulatory, accounting, or eligibility questions.Connector
- The unit tests (code examples) for HMR. Always call `learn-hmr-basics` and `view-hmr-core-sources` to learn the core functionality before calling this tool. These files are the unit tests for the HMR library, which demonstrate the best practices and common coding patterns of using the library. You should use this tool when you need to write some code using the HMR library (maybe for reactive programming or implementing some integration). The response is identical to the MCP resource with the same name. Only use it once and prefer this tool to that resource if you can choose.Connector
- Switch the app's V1 CI from "boot the real app + deps" mode to sandbox mode (mocks fetched by content-hash from the cloud canonical pool). The doc-stated trigger: ~1 week after CI is wired, when the dev has felt the slow runs / flakes and you can pitch "your CI takes 90s and flaked twice this week — rerecord mocks and CI drops to ~8s." What flips: * The CI workflow YAML gets a --sandbox flag on `keploy test-gen run` and the docker-compose-up step removed. This tool returns the updated YAML; you re-PR it. Pre-condition: every resource you want in CI must have recorded mocks (config.yaml.mockRegistry.mock populated). Resources without mocks will fail in sandbox mode because there's nothing to serve. Run devloop_record_sandbox per resource first; verify via devloop_schema_drift_report-style checks before proposing the switch.Connector
- Submit a trading-edge idea to the governed edge-idea bounty. You are paid a FLAT sats bounty for the IDEA if it survives the same backtest gate (Monte-Carlo permutation p-value + Deflated Sharpe) our own live Bitcoin bot is held to — no capital is pooled, you keep your funds, we buy the idea. Tiers auto-detected from `spec`: parameter (a search grid on an existing strategy family), code (a novel signal function — run only in a hardened, network-off Docker sandbox), or concept (a free-text idea). A code-tier signal_code must define generate_signals(candles).Connector
- Run declarative assertions on an agent trace (OpenAI tool-call messages, LangChain run trees, or plain text logs). No LLM call — deterministic. Assertion types: order (tool A before B), must_call, must_not_call, max_calls, min_calls, no_error, recovery (agent continues after error). Returns per-assertion PASS/FAIL, parsed steps, and an overall verdict. Use this to gate CI/CD on agent behavior correctness.Connector
- View applications for your listing. Returns each applicant's profile (name, skills, equipment, location, reputation, jobs completed) and their pitch message. Use this to evaluate candidates, then hire with make_listing_offer. Only the listing creator can view applications.Connector