153,272 tools. Last updated 2026-05-28 17:43
"Managing Docker Containers and Applications" matching MCP tools:
- 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.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
- Use this read-only tool to retrieve SEC XBRL-backed fundamentals for one crypto public company ticker. It returns filing period, entity identifiers, filing form, core financial values, provenance, and optional segment or related-party containers when requested. Parameters: ticker is required; period is optional YYYY-MM-DD; include_segments and include_related_party request additional containers when available and otherwise return availability metadata. Behavior: read-only and idempotent; it performs one HTTPS read, has no destructive side effects, and does not modify SEC data, accounts, files, or wallets. Use it when the user asks for revenue, net income, assets, cash, liabilities, equity, SEC filing context, or fact provenance; use alpha_signals or covenant_stress for modeled signal interpretation.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
- Fetches any public web page and returns clean, readable plain text stripped of HTML, navigation, scripts, advertisements, and boilerplate. Returns the page title, meta description, word count, and main body text ready for analysis or summarisation. Use this tool when an agent needs to read the content of a specific web page or article URL — for example to summarise an article, extract facts from a page, verify a claim by reading the source, or convert a web page into plain text to pass to another tool. Pass article URLs returned by web_news_headlines to this tool to read full article content. Do not use this tool to discover current news headlines — use web_news_headlines instead. Does not execute JavaScript — best suited for standard HTML content pages. Will not work with paywalled, login-protected, or JavaScript-rendered single-page applications.Connector
- 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.Connector
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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.Connector
- 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, found NO learning for this specific problem (only unrelated or tangential hits), and solved it — then offer to post ✓ You discovered deprecated APIs, breaking changes, or new best practices not already documented ✓ The solution took meaningful debugging effort (5+ minutes) ✓ It's generic enough to help other agents (not company-specific code) WHEN NOT TO USE (use convergence tools instead): ✗ Search returned a learning for the same problem — use suggest_edit, add_addendum, or edit notes; duplicate posts hurt search quality ✗ Your contribution is only a variant, extra tip, or "what worked for me" on an existing fix — suggest_edit or add_addendum ✗ You want to link two related but distinct issues — link_learnings with relates_to, not a second full learning EFFORT METRICS (OPTIONAL): - tokens_used: include if your runtime tracks token usage. Powers the aggregate agent effort saved counter. - solve_time_minutes: rough estimate of debugging time. Optional fallback signal. Omitting both is fine. Don't fabricate numbers — leave blank if you don't know. 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] 📄 Problem: [problem preview] 📄 Solution: [solution preview] 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. STRUCTURED SECTIONS (REQUIRED problem + solution; optional cause + notes): • problem — specific symptom or error (searchable, max 500 chars) • cause — root cause / why it happens (optional, max 1000 chars). Skip if no distinct cause. • solution — the fix, with code if needed (max 5000 chars) • notes — edge cases, version caveats (optional, max 2000 chars) 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.Connector
- Apply to work on a published task. Workers can browse available tasks and apply to work on them. The agent who published the task will review applications and assign the task to a chosen worker. Requirements: - Worker must be registered in the system - Task must be in 'published' status - Worker must meet minimum reputation requirements - Worker cannot have already applied to this task Args: params (ApplyToTaskInput): Validated input parameters containing: - task_id (str): UUID of the task to apply for - executor_id (str): Your executor ID - message (str): Optional message to the agent explaining qualifications Returns: str: Confirmation of application or error message. Status Flow: Task remains 'published' until agent assigns it. Worker's application goes into 'pending' status.Connector
- WORKFLOW: Step 1 of 4 - Start infrastructure design conversation Open an InsideOut V2 session and receive the assistant's intro message. The response contains a clean message from Riley (the infrastructure advisor) - display it to the user. ⚠️ Riley will ask questions - forward these to the user, DO NOT answer on their behalf. CRITICAL: This tool returns a session_id in the response metadata. You MUST use this session_id for ALL subsequent tool calls (convoreply, tfgenerate, tfdeploy, etc.). ⚠️ The session_id includes a ?token=... suffix (format: sess_v2_xxx?token=yyy) which is part of the session credential — without it, downstream tools fall back to a tokenless connect URL that 401s. Always pass session_id verbatim to subsequent tools and to the user; do NOT shorten, paraphrase, or strip the ?token= portion when summarizing the session in chat or in your own scratch notes. Use when the user mentions keywords like: 'setup my cloud infra', 'provision infrastructure', 'deploy infra', 'start insideout', 'use insideout', or similar intent to begin infra setup. OPTIONAL: project_context (string) - General tech stack summary so Riley can skip discovery questions and jump to recommendations. The agent should confirm this with the user before sending. Include whichever apply: language/framework, databases/services, container usage, existing IaC, CI/CD platform, cloud provider, Kubernetes usage, what the project does. Example: 'Next.js 14 + TypeScript, PostgreSQL, Redis, Docker Compose, deployed to AWS ECS, GitHub Actions CI/CD, ~50k MAU'. NEVER include credentials, secrets, API keys, PII, source code, or internal URLs/IPs -- only general metadata summaries useful to a cloud architect agent. IMPORTANT: source (string) - You MUST set this to identify which IDE/tool you are. Auto-detect from your environment: 'claude-code', 'codex', 'antigravity', 'kiro', 'vscode', 'web', 'mcp'. If unsure, use the name of your IDE/tool in lowercase. Do NOT omit this — it controls the 'Open {IDE}' button on the credential connect screen. OPTIONAL: github_username (string) - GitHub username for deploy commit attribution. Pre-populates the GitHub username field on the connect page. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.Connector
- Get real-time natural disaster alerts from USGS (earthquakes M5.0+), NOAA (hurricanes, tropical storms), and GDACS (global earthquakes, cyclones, floods, volcanoes). Returns active and recent events with magnitude, severity, coordinates, and affected country. Used by logistics planners and procurement teams to reroute shipments and activate contingency plans around seismic events, hurricanes, and floods affecting supply chain infrastructure.Connector
- Retrieve the full GLEIF LEI record for one legal entity using its 20-character LEI code. Returns legal name, registration status, legal address, headquarters address, managing LOU, and renewal dates. Use this tool when: - You have a LEI (from SearchLEI) and need full entity details - You want to verify the registration status and renewal date - You need the exact legal address and jurisdiction of an entity Source: GLEIF API (api.gleif.org). No API key required.Connector
- Browse the knowledge base by technology tag at the START of a task. Call this when beginning work with a specific technology to discover what verified knowledge already exists — before you hit problems. Examples of useful tags: 'pytorch', 'cuda', 'fastapi', 'docker', 'ros2', 'numpy', 'jetson', 'arm64', 'postgresql', 'redis', 'kubernetes', 'react'. Returns a list of questions (title + tags + score) for the given tag, ordered by community score. Call `get_answers` on relevant results.Connector
- Deploy a project to the staging environment. This triggers: (1) Schema validation, (2) Docker image build, (3) GitHub commit, (4) Kubernetes deployment, (5) Database migrations. The operation is ASYNCHRONOUS - it returns immediately with a job_id. Use get_job_status with the job_id to monitor progress. Deployment typically takes 2-5 minutes depending on schema complexity. If deployment fails, check: (1) Schema format is FLAT (no 'fields' nesting), (2) Every field has a 'type' property, (3) Foreign keys reference existing tables, (4) No PostgreSQL reserved words in table/field names. Use get_project_info to see if the deployment succeeded.Connector
- WORKFLOW: Step 1 of 4 - Start infrastructure design conversation Open an InsideOut V2 session and receive the assistant's intro message. The response contains a clean message from Riley (the infrastructure advisor) - display it to the user. ⚠️ Riley will ask questions - forward these to the user, DO NOT answer on their behalf. CRITICAL: This tool returns a session_id in the response metadata. You MUST use this session_id for ALL subsequent tool calls (convoreply, tfgenerate, tfdeploy, etc.). ⚠️ The session_id includes a ?token=... suffix (format: sess_v2_xxx?token=yyy) which is part of the session credential — without it, downstream tools fall back to a tokenless connect URL that 401s. Always pass session_id verbatim to subsequent tools and to the user; do NOT shorten, paraphrase, or strip the ?token= portion when summarizing the session in chat or in your own scratch notes. Use when the user mentions keywords like: 'setup my cloud infra', 'provision infrastructure', 'deploy infra', 'start insideout', 'use insideout', or similar intent to begin infra setup. OPTIONAL: project_context (string) - General tech stack summary so Riley can skip discovery questions and jump to recommendations. The agent should confirm this with the user before sending. Include whichever apply: language/framework, databases/services, container usage, existing IaC, CI/CD platform, cloud provider, Kubernetes usage, what the project does. Example: 'Next.js 14 + TypeScript, PostgreSQL, Redis, Docker Compose, deployed to AWS ECS, GitHub Actions CI/CD, ~50k MAU'. NEVER include credentials, secrets, API keys, PII, source code, or internal URLs/IPs -- only general metadata summaries useful to a cloud architect agent. IMPORTANT: source (string) - You MUST set this to identify which IDE/tool you are. Auto-detect from your environment: 'claude-code', 'codex', 'antigravity', 'kiro', 'vscode', 'web', 'mcp'. If unsure, use the name of your IDE/tool in lowercase. Do NOT omit this — it controls the 'Open {IDE}' button on the credential connect screen. OPTIONAL: github_username (string) - GitHub username for deploy commit attribution. Pre-populates the GitHub username field on the connect page. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.Connector
- Scaffold the GitHub Actions workflow that runs the V1 API tests on every PR. Returns the exact YAML content to write to .github/workflows/keploy.yml + the Bash command to set the KEPLOY_API_KEY secret. The AI walks the playbook with its Write tool + the `gh` CLI. PRECONDITIONS — CHECK BEFORE CALLING. Calling this tool out of order is a DEVLOOP violation; the doc-stated user-flow ordering is generate → run → mutation-prove (opt-in) → expand (opt-in) → CI (opt-in). Specifically you must have: 1. Generated at least one test via devloop_generate_resource_flow AND watched it pass via "keploy test-gen run --ci". 2. SURFACED the mutation-prove opt-in to the dev verbatim: "Want me to prove the test catches bugs by applying 3 small mutations to your handler and reverting?" — and the dev answered (yes-walked through devloop_mutation_demo, or explicit no/skip/later). Doing the test runs is NOT the same as offering mutation-prove; the offer is a separate dev-facing question. 3. ASKED the dev "want me to wire this into CI?" — explicit yes from the dev. If ANY of those three are missing, STOP and back up. The mutation-prove gate is what builds the dev's trust before they commit Keploy to CI; skipping it ships shallow tests into a workflow the dev hasn't validated. What this tool does NOT do (intentionally — the dev keeps custody): * Mint the CI API key server-side. The dev provisions it themselves in the Keploy dashboard (Step 2 of the returned playbook walks them through it). The AI never sees the kep_* value — it transits dashboard clipboard → terminal stdin → gh CLI's encrypted POST. This is a security property, not a limitation. * Post structured PR comments from api-server. V1 relies on GitHub Actions' native status-check rendering; the structured comment renderer is a V1.5 lift. The emitted workflow runs on pull_request (default base branch) and reads app_id / test-dir / context-dir from keploy/api-tests/keploy-test-gen.yaml — the dev never has to thread flags through the workflow. TIME-FREEZING — DEFAULT ON, ALMOST ALWAYS NEEDED FOR BACKEND APPS. Almost every backend app has authentication (login → JWT/session/OAuth). The dev's recorded tests carry those tokens in headers. Between record time and the first PR's CI run, the tokens' exp claims pass real wall-clock — CI then 401s on every authenticated step, and the dev blames Keploy. Keploy's time-freezing rewinds the app's clock to the record moment so the recorded tokens validate. Default policy: time_freezing=true. The AI MUST inspect the dev's test suites BEFORE calling this tool: - <app_dir>/keploy/api-tests/<resource>/test.yaml (V1 sources) - <app_dir>/keploy/<SuiteName>/tests/*.yaml (captured sandbox tests) Look for: Authorization Bearer headers; steps hitting /login /auth /signin /token /oauth; response bodies containing jwt / token / access_token / refresh_token / expires_in / iat / exp. If any of those signals appear (or you're unsure), keep time_freezing=true. Only pass time_freezing=false when you've audited every suite and confirmed zero time-sensitive tokens (rare for a real backend). When time_freezing=true, this tool also requires app_language (go / node / python / java / ruby / other) and app_service (docker-compose service name). Output then includes: - Modified workflow YAML (pre-populates keploy-sockets-vol; uses -f docker-compose.yml -f docker-compose.keploy.yml; passes --freezeTime) - docker-compose.keploy.yml override (volume mount + LD_PRELOAD for non-Go, or Dockerfile.keploy build for Go) - Dockerfile.keploy (Go ONLY — vDSO bypasses LD_PRELOAD, requires -tags=faketime rebuild) The dev's plain "docker compose up" is unaffected. Time-freezing only activates when CI (or the dev locally) explicitly passes both compose files. TIME-FREEZING IS REPLAY-ONLY — STRICT INVARIANT. The Dockerfile.keploy / docker-compose.keploy.yml / --freezeTime flag this tool emits exist purely to make recorded JWTs validate at REPLAY time. They MUST NEVER apply when recording. Concretely: - Record uses the dev's PROD Dockerfile + plain "docker compose up" (no override file). - Replay uses Dockerfile.keploy + "docker compose -f docker-compose.yml -f docker-compose.keploy.yml up" + the --freezeTime flag on the CLI. If a recording is captured against a faketime-built binary, every timestamp in the captured mocks is wrong and the whole capture is corrupt — there is no recovery short of re-recording from scratch with the prod binary. The CI YAML this tool emits in ci_mode=sandbox-replay is a REPLAY workflow; it boots via the compose override on purpose. The dev's separate record flow (devloop_record_sandbox) must NOT touch the override. TIME-FREEZING IS FORCED ON FOR ci_mode=sandbox-replay — NON-NEGOTIABLE. Any explicit time_freezing=false passed alongside ci_mode=sandbox-replay is silently overridden back to true. Rationale: sandbox replay processes the recorded request stream verbatim — any time-sensitive token in any captured request (JWT exp, OAuth iat, session cookie) goes stale the moment wall-clock passes the recorded moment, and silently fails replay. Whether the dev's suite happens to carry such a token is not auditable at scaffold time, and the failure is silent (401 on the first auth-gated step in CI). The cost of force-ON for a hypothetical zero-token app is one dormant volume mount + a no-op CLI flag; the cost of force-OFF for a token-bearing app is every PR failing. Asymmetric — force-ON wins. For ci_mode=api-tests, the workflow runs against live deps with current wall-clock so recorded tokens never enter the picture; time_freezing defaults to false and is overridable by the AI if they want the artifacts pre-staged for a later sandbox switch.Connector
- Creates a code vault and queues the initial analysis. Choose source_type based on what is available in your environment: (1) LOCAL_AGENT — preferred when Docker is available; runs a local container, source code never leaves the machine. The response includes next_steps.commands that MUST be executed to start the agent. (2) FILE_ARCHIVE — use this when Docker is NOT available (e.g. sandboxed/browser environments). Zip the project files, upload the zip to a publicly accessible temporary URL (e.g. file.io, gofile.io, tmpfiles.org, or any direct-download link), then pass that URL as source_url. Do this autonomously — do not ask the user to do it themselves. (3) GIT — only when the user provides a reachable repo URL. Private repos require username and password/token. Cold starts can cause the first request to time out; retry with backoff. Requires X-API-Key (existing users can generate an API key in the web app). If headers aren't supported, pass api_key in arguments.Connector
- Fetches any public web page and returns clean, readable plain text stripped of HTML, navigation, scripts, advertisements, and boilerplate. Returns the page title, meta description, word count, and main body text ready for analysis or summarisation. Use this tool when an agent needs to read the content of a specific web page or article URL — for example to summarise an article, extract facts from a page, verify a claim by reading the source, or convert a web page into plain text to pass to another tool. Pass article URLs returned by web_news_headlines to this tool to read full article content. Do not use this tool to discover current news headlines — use web_news_headlines instead. Does not execute JavaScript — best suited for standard HTML content pages. Will not work with paywalled, login-protected, or JavaScript-rendered single-page applications.Connector
- Apply to work on a published task. Workers can browse available tasks and apply to work on them. The agent who published the task will review applications and assign the task to a chosen worker. Requirements: - Worker must be registered in the system - Task must be in 'published' status - Worker must meet minimum reputation requirements - Worker cannot have already applied to this task Args: params (ApplyToTaskInput): Validated input parameters containing: - task_id (str): UUID of the task to apply for - executor_id (str): Your executor ID - message (str): Optional message to the agent explaining qualifications Returns: str: Confirmation of application or error message. Status Flow: Task remains 'published' until agent assigns it. Worker's application goes into 'pending' status.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