Skip to main content
Glama
152,396 tools. Last updated 2026-05-28 14:03

"Proton Drive" matching MCP tools:

  • **Retrofit a phone-control link onto an EXISTING channel.** Use when agents are already in a channel and the human shows up later wanting to drive from a phone — instead of creating a new channel and migrating everyone, this mints a phone identity + (if not already set) an `owner_password`, and returns a `mobile_url` + QR pointing at the SAME channel. Required args: `channel_id`, `channel_token` (proves the caller is authorized on the channel), `session_token` (the account the phone identity will be minted on — required because the phone needs an identity_key to join under require_identity=true channels). Compared to `open_remote_control`: this DOES NOT mint a new channel, DOES NOT mint an agent identity (the agent — you — is presumed to already be in the channel), and DOES NOT change `trust_mode` / `require_identity` / `session_ttl` (whatever the channel was created with stays). It only adds the phone affordance. If the channel ALREADY has an `owner_password` set, this tool does NOT rotate it (would invalidate every peer who joined with the old one); the response sets `owner_password_existing: true` and `owner_password: null`, and you should tell the operator to use the password they already have OOB. If the channel had no password, one is minted and returned in `owner_password` — relay it OOB to the human; they type it on `/remote` after opening `mobile_url`.
    Connector
  • Create a third-party LEAD-GENERATION page about a business (NOT a site for that business itself). Use this when the goal is to drive qualified search traffic to someone else's business — affiliate pages, review/guide pages, niche directories. The page is branded as an outside guide (e.g. "Best Roofers in San Diego"), refers to the business in the third person, and routes CTAs to the business's existing website. Differences from create_site: - Slug + page brand are SEO-vanity (e.g. "best-roofers-sandiego"), not the candidate's brand name. - Voice is third-party guide/reviewer — never first person. - Primary CTA is "visit their website"; phone/email demoted. - No specific pricing quoted; differentiators emphasized. - Locality is judged by category, not just address (IT/SaaS/agency stays category-wide even when a city is on file). Pass a business candidate object from search_businesses — that business is the one being PROMOTED. Requires authentication via API key (Bearer token). Generate an API key at webzum.com/dashboard/account-settings. The page generation happens in the background. Use get_site_status to check progress. Returns the businessId (a vanity slug) which can be used to access the page at /build/{businessId}.
    Connector
  • Extract structured transaction data from a contract at a URL. Downloads the document, extracts text (with OCR fallback for scanned PDFs), and runs PrimaCoda's contract-extraction prompt to return parties, addresses, dates, prices, and key contract fields. Use this when an agent has the contract hosted somewhere (Dropbox, Google Drive direct download, Square Space, etc.) and wants to skip the upload step. For multi-document deals (purchase + addenda + disclosures), use the PrimaCoda dashboard's batch upload — this tool handles ONE document. Args: pdf_url: Direct download URL for the contract (PDF, DOCX, TXT, or image). Must be reachable from the PrimaCoda server. Google Drive "shared link" URLs work if set to "anyone with link"; other share URLs may need their direct-download form. api_key: Your PrimaCoda MCP API key (starts 'pck_').
    Connector
  • DESTRUCTIVE — IRREVERSIBLE. Permanently delete a file from the user's Drive. Removes the file from S3 storage and the database. Storage quota is freed immediately. ALWAYS ask for explicit user confirmation before calling this tool. # delete_file ## When to use DESTRUCTIVE — IRREVERSIBLE. Permanently delete a file from the user's Drive. Removes the file from S3 storage and the database. Storage quota is freed immediately. ALWAYS ask for explicit user confirmation before calling this tool. ## Parameters to validate before calling - file_token (string, required) — The file token (UUID) of the file to delete. Get via fetch_files. ## Notes - DESTRUCTIVE — IRREVERSIBLE. Always confirm with the user before calling. Explain what will be lost.
    Connector
  • Search the company's connected knowledge across every source — Drive, SharePoint, Confluence, Slack, Notion — with cited synthesized answers, lifecycle awareness, and refusal-on-weak-context. Returns a written answer with [n] citations plus the ranked source chunks. Modes: `fast` (1,500 kT — retrieval-only, no synthesis), `standard` (12,500 kT — default; synthesized answer over the top retrieval set), `deep` (25,000 kT — wider retrieval + premium synthesis for complex questions). Pick the cheapest tier that answers the question. Responses are capped at 25,000 output tokens per Claude Connectors policy; if truncated, structured metadata carries `truncated: true` and `query_id` so the agent can call `get_source_detail` for full provenance.
    Connector
  • Logic-trace driver-chain explorer — answers "WHY is this activity critical?" and "WHAT does it drive?". Traces driving predecessors backward from a target activity to project start (the "why critical" chain) and/or driving successors forward to project finish (the "what it drives" chain). Detects constraint-driven artificial criticality and cites AACE RP 24R-03 §4 when found. Supports multiple parallel critical paths (MCPM) and near-critical paths. Use this tool when investigating a single activity's logic chain. For a project-wide CP / logic health audit, use ``critical_path_validator``. Args: xer_path: server-side path to the schedule XER. xer_content: full text of the schedule XER (alternative for hosted/remote use). Supply EXACTLY ONE of path/content. target_activity_codes: list of task_codes to trace; if empty, all CP / near-critical endpoints are traced. direction: 'backward' (predecessors), 'forward' (successors), or 'both' (default). include_near_critical: also trace near-critical endpoints (within float band). output_dir: optional dir for HTML / CSV / JSON outputs. Returns: { "paths": [{chain dicts ...}], "output_files": {dashboard, csv, json}, "project_finish": "YYYY-MM-DD", "project_name": ..., "data_date": ... }
    Connector

Matching MCP Servers

Matching MCP Connectors

  • Google Drive MCP Pack

  • Real-time weather conditions, Dog Walk Index scores, and drive route rain checks for any US city. Three tools: get_weather, check_drive_rain, get_forecast. No API key required.

  • Drive a headless Chromium against a URL and return a screenshot for each requested viewport (mobile / tablet / desktop). Optional clickPaths lets you grab the state behind a sequence of clicks (e.g. ['Sign in', '#email', 'Continue']). Pricing: 1 credit per single viewport, 5 credits for the desktop+tablet+mobile triple (otherwise 1 × viewport count). Output: signed Spaces URLs valid for 7 days. Use this for marketing screenshots, design QA, regression-watch baselines — anything where you need pixels without a full AI test.
    Connector
  • Return modules that have a typed compatibility relationship with the given module. Both edge directions are returned and tagged via the per-match `direction` field — so a single call answers both "what is X a R for?" and "what is a R for X?". Use this for two question shapes: 1. Patch-time compatibility — "what could I use as a clock source for X?" (returns matches with direction='inbound'), or "what does X clock?" (direction='outbound'). 2. Catalog comparison — "what's an alternative to X?" (symmetric), "what does X replace?" (outbound) / "what replaces X?" (inbound), "is there an expander for X?" (inbound). The relationship is required and typed — no fuzzy matching. The vocabulary describes the edge as stored (from = role-bearer, to = target): Patch-time: - clock-source-for — A clocks B - cv-source-for — A produces CV that B consumes - modulator-for — A is a modulator suitable for B (LFO, S&H, random) - audio-source-for — A is an audio source for B (typically a VCO into a VCF) - quantizer-for — A quantizes for B - trigger-source-for — A produces triggers that B consumes - envelope-target-for — A is something B's envelope output is designed to drive Catalog: - replaces — A is the newer successor to B (Morphagene replaces Phonogene) - alternative-to — symmetric: A and B occupy similar design space with different character - expander-for — A is an expander module for the host module B Direction tag on each match: - outbound: queried module is the FROM side (role-bearer). Match is what the queried module does as R. - inbound: queried module is the TO side. Match is the R-for the queried module. - symmetric: only for alternative-to. Args: - module_id (string, required): "<manufacturer-slug>/<module-slug>" - relationship (string, required): one of the values above - limit (number): default 50, max 200 Returns: { "module": { id, name }, "relationship": <relationship>, "matches": [{ id, name, manufacturer, notes, source_id, direction }] } If the module is unknown, returns an error. If no relationships have been recorded in either direction, returns matches=[]. The `notes` field describes the edge in the canonical A→B direction; combined with `direction` the caller can read it correctly either way.
    Connector
  • Predict the VAS (Viewability Attention Score) a specific creative would achieve at a given moment, based on historical data and causal modeling. Uses the CausalPredictionService which: 1. Embeds the moment description to find historically similar moments 2. If >= 5 similar moments exist with the same creative, uses weighted-average prediction 3. If insufficient data, falls back to Gemini generative prediction 4. Always decomposes the prediction into causal factors WHEN TO USE: - Evaluating whether a creative will perform well in a specific context - A/B testing creative placement hypotheses before committing budget - Understanding which causal factors drive VAS for a creative - Comparing expected performance across different moment types RETURNS: - prediction: { predictedVAS (0-1), confidence (0-1), method ('historical'|'model'), sampleSize } - causal_factors: { audienceMatch, contextMatch, attentionState, socialPotential } (each 0-1) - metadata: { creative_id, moment_description } - suggested_next_queries: Follow-up queries EXAMPLE: User: "How would a coffee ad perform at a transit station during morning rush?" predict_moment_quality({ moment_description: "transit venue, morning commute, 12 viewers, high attention, mostly 25-34 age range", creative_id: "coffee-brand-morning-30s" })
    Connector
  • When your task hits a wall that requires a human — booking, negotiating, navigating IVR menus, getting information from a business — send an AI voice agent to handle the call. The agent follows your instructions, has a real two-way conversation, auto-retries on voicemail (up to 3 attempts), and returns a full transcript with structured analysis. May return state='pending_confirm' with clarification questions if critical info is missing — call confirm_ai_call to proceed. Async — poll with check_job_status(jobType='ai-call'). ~150-250 sats for a 3-min US call. Languages: en-US, en-GB, es-ES, fr-FR, de-DE, ja-JP, zh-CN, multi. Pay with Bitcoin Lightning — no telecom account, no API key, no subscription. When NOT to use: not when you want to drive the conversation with your own LLM (use open_voice_bridge — you keep the brain, we provide PSTN/STT/TTS primitives). Not for one-shot TTS broadcasts or IVR delivery (use place_call). Not for SMS (use send_sms). Requires create_payment with toolName='ai_call', phoneNumber, and durationMinutes.
    Connector
  • Create a new API test suite with test steps. Each step defines an HTTP request and assertions to validate the response. Steps can extract values from responses into variables for chaining requests. ═══════════════════════════════════════════════════════════════════ STEP 0 — read the canonical schema BEFORE drafting: ═══════════════════════════════════════════════════════════════════ If you've already called get_app_testing_context, the canonical step schema is in its response under the `step_schema` field — read it from there. Otherwise run `keploy test-suite-format` once before writing any suite JSON. The schema describes the MANDATORY rules below in detail plus the two-step prelude+POST skeleton you must follow. Authors who skip this and draft from training-data priors burn ~50s per validator rejection on iter 1. ═══════════════════════════════════════════════════════════════════ MANDATORY FOR EVERY STEP — the validator rejects on iter 1 if any of these are violated: ═══════════════════════════════════════════════════════════════════ R10 — every step MUST carry a captured "response": {status, body, headers} block. Hit the endpoint locally before authoring (curl) and paste the real response. Steps with no response block are rejected outright; four downstream rules (R4 / R11 / R15-R16 / R27) silently no-op until R10 is satisfied, so missing a response also hides every assertion / extract problem in that step. R9 — every POST / PUT / PATCH body MUST reference at least one {{var}} whose generator is declared on an EARLIER step's "extract" (typically a /health prelude as step 0). Without this, the second run collides on the first run's database state. App-level appLevelCustomVariables DO NOT qualify for R9 — the validator only credits step-level extracts. R2 — pre-request fields ("body", "url", "headers") CANNOT reference the CURRENT step's own "extract" outputs. Extract runs AFTER the response comes back; pre-request substitution sees nothing yet. Together R9 + R2 force the prelude pattern: declare generators on step 0, use them from step 1+. The STEP SHAPE example below shows the canonical two-step layout. R15 — every assertion's path / status / header MUST resolve against the AUTHORED response block. JSONPath uses gjson dot-array syntax: $.orders.0.id — NOT $.orders[0].id (the bracket form does not resolve in gjson; the assertion is rejected as "key not present in recorded body"). For status_code / header_* assertions, the values must match what's in response.status / response.headers verbatim — capture the real response via curl before authoring. R32 — every step-level extract key MUST NOT collide with the app's appLevelCustomVariables (enumerate them via get_app_testing_context or getApp before authoring). The runtime's variable lookup resolves app-level first, so a colliding key means the suite's function silently never runs. Suite-suffix when in doubt: userNonceForSuite, not genUserId. Don't invent a parallel generator with the same name as an existing app-level one. ═══════════════════════════════════════════════════════════════════ GRAPHQL APPS — read this section FIRST when the app's primary endpoint is /graphql (or any other path that accepts a GraphQL request envelope). REST apps can skip this section entirely. ═══════════════════════════════════════════════════════════════════ WHEN GRAPHQL VALIDATION RUNS — automatic dispatch, no flag to set: The validator inspects each step's request body. If ANY step's body parses to a JSON envelope containing a non-empty "query" string OR an "extensions.persistedQuery.sha256Hash" (APQ flow), the ENTIRE suite routes through the GraphQL validator (G-rules). Otherwise it routes through the REST validator (Charan's R-rules described above). Decision is per-suite, NOT per-step. Consequences: - Pure GraphQL suite (every step is POST /graphql with envelope body) → G-rules - Pure REST suite (no step's body looks like a GraphQL envelope) → R-rules - MIXED suite (some REST steps, some GraphQL steps in ONE suite) → tilts GraphQL and REST steps fail G1 envelope check with a clear error Rule of thumb: DO NOT mix in one suite. Author one suite per surface — separate REST suites for REST endpoints, separate GraphQL suites for /graphql. App-level both is fine; suite-level mixing is rejected by design. GRAPHQL SUITE SHAPE — every step is POST to /graphql with body = { "query": "<operation document>", "variables": { <map of $variable values, optional> }, "operationName": "<name>" // required when query has multiple operations } Body is a JSON-encoded STRING (same convention as the REST shape). URL is always /graphql. Method is always POST. headers must include Content-Type: application/json. PLACEHOLDER POSITION — substitute {{var}} INSIDE envelope.variables values, NEVER inside the query string: ✓ "body":"{\"query\":\"query Q($id: ID!){user(id:$id){id email}}\", \"variables\":{\"id\":\"{{createdUserId}}\"},\"operationName\":\"Q\"}" ✗ "body":"{\"query\":\"query{user(id:\\\"{{createdUserId}}\\\"){id email}}\"}" The second form fires G23 — placeholder inside query string. Substitution at the document level injects raw text into syntactic positions where it doesn't belong; the resulting query may parse weirdly server-side. Always use the variables map. OPERATION-TYPE SEMANTICS — the validator parses the query and detects whether the active operation is query / mutation / subscription: - mutation operations must include ≥1 dynamic value in envelope.variables (G24). Idempotent mutations (e.g. mutation { logout }) waive the check via step.idempotent:true. - subscription operations are REJECTED outright (G25). Live-run is request/response only; streaming subscriptions can't be exercised here. Author a separate harness for those. - query operations have no dynamism requirement (reads are inherently idempotent). EXTRACT PATHS for GraphQL responses — paths always start with $.data.* or $.errors.* (the GraphQL response envelope wraps everything under one of those keys): ✓ extract: { "createdId": "$.data.createUser.id" } ✓ extract: { "firstErrCode": "$.errors.0.extensions.code" } ✗ extract: { "createdId": "$.id" } ← fires G30, fails to resolve Aliased fields: if a query aliases a field ('me: user(id: ...)'), the extract path must reference the ALIAS, not the underlying field: ✓ query { me: user(...){ id } } → extract: { "userId": "$.data.me.id" } ✗ query { me: user(...){ id } } → extract: { "userId": "$.data.user.id" } ← fires G32 GENERATOR PLACEMENT — fn-generator {{var}} values (backed by an "extract" entry whose value starts with "function name(){...}") re-evaluate on EVERY substitution call. They're correct ONLY in mutation envelope.variables for the FIRST time a value is created: ✓ Step 1 (mutation): variables.email = "{{regenEmail}}" ← fresh email per run, OK ✗ Step 2 (query): variables.id = "{{regenEmail}}" ← G28 rejects (regenerator returns a fresh value the server never persisted) ✗ assert[N].expected = "{{regenEmail}}" ← G27 rejects (re-evaluates at assert time, won't match the value the request body sent) For query variables / assert.expected, ALWAYS reference a JSONPath extract from a prior creating step: Step 1 (mutation): extract: { "createdUserId": "$.data.createUser.id" } Step 2 (query): variables: { "id": "{{createdUserId}}" } ← stable, OK Step 2 (query): assert: [{ ..., "expected": "{{createdUserId}}" }] ← stable, OK ASSERTION PATTERNS for GraphQL — every step needs MORE than status_code (GraphQL returns 200 even when errors[] is populated; bare status_code:"200" hides real resolver failures): - Happy-path steps: include {"type":"graphql_no_errors"} — verifies errors[] is empty (G40). Bare status_code without graphql_no_errors fires G40. - Error-path steps: set step.expect_error:true AND include at least one of: {"type":"graphql_has_error_code", "expected":"<code>"} {"type":"graphql_error_path_eq", "expected":"<dotted.path>"} {"type":"graphql_error_message_contains", "expected":"<substring>"} An expect_error:true step without any error-shape assertion fires G42. - DON'T use ONLY status_code in any GraphQL step — pair with body-level assertions (G41). CANONICAL HAPPY-PATH SHAPE — three steps, prelude + mutation + read-back: Step 0 (prelude, idempotent:true): body: {"query":"{ __typename }"} extract: { "regenEmail": "function regenEmail(){return 'u_'+Date.now()+'_'+Math.random().toString(36).slice(2,8)+'@x.com';}" } assert: [{"type":"status_code","expected":"200"}, {"type":"graphql_no_errors"}] Step 1 (mutation): body: {"query":"mutation M($input: CreateUserInput!){ createUser(input: $input){ id email name role } }", "variables":{"input":{"email":"{{regenEmail}}","name":"Alice","role":"USER"}},"operationName":"M"} extract: { "createdId": "$.data.createUser.id", "createdEmail": "$.data.createUser.email" } assert: [{"type":"status_code","expected":"200"}, {"type":"graphql_no_errors"}, {"type":"json_equal","key":"$.data.createUser.role","expected":"USER"}] Step 2 (query read-back): body: {"query":"query Q($id: ID!){ user(id: $id){ id email name role } }", "variables":{"id":"{{createdId}}"},"operationName":"Q"} assert: [{"type":"status_code","expected":"200"}, {"type":"graphql_no_errors"}, {"type":"json_equal","key":"$.data.user.id","expected":"{{createdId}}"}, {"type":"json_equal","key":"$.data.user.email","expected":"{{createdEmail}}"}] CANONICAL ERROR-PATH SHAPE — single step that intentionally triggers a server-side error: { "name":"createUser with duplicate email", "method":"POST", "url":"/graphql", "headers":{"Content-Type":"application/json"}, "body":"{\"query\":\"mutation M($input: CreateUserInput!){ createUser(input:$input){id} }\", \"variables\":{\"input\":{\"email\":\"seed@example.com\",\"name\":\"Dup\",\"role\":\"USER\"}},\"operationName\":\"M\"}", "expect_error": true, // tells G42 this is an error-path step "idempotent": true, // step is idempotent — always errors with same code "response": { "status": 200, "headers": {"Content-Type":"application/json"}, "body": "{\"data\":null,\"errors\":[{\"message\":\"user with email \\\"seed@example.com\\\" already exists\",\"path\":[\"createUser\"],\"extensions\":{\"code\":\"EMAIL_EXISTS\"}}]}" }, "assert": [ {"type":"status_code","expected":"200"}, {"type":"graphql_has_error_code","expected":"EMAIL_EXISTS"}, {"type":"graphql_error_path_eq","expected":"createUser"} ] } ═══════════════════════════════════════════════════════════════════ APP CONFIG FIRST — read the app before authoring: ═══════════════════════════════════════════════════════════════════ Before any other step, call getApp({app_id}) and read these fields: * appLevelCustomVariables — dynamic generators and static fixtures pre-configured by the dev, shared across every suite for this app. Common shapes: - genUserId, genProductName (JS functions returning fresh entropy per run, e.g. `alice_<rand1-10000>`) - staticUser (a fixed user the dev wants tests to use) - zeroQuantity, negativePrice, invalidUser (static fixtures for validation tests) PREFER these over inventing your own JS-function in `extract`. They're the dev's authoritative dynamic-input set — using them in POST/PUT/PATCH bodies via `{{varName}}` means each replay hits a fresh row, sidestepping duplicate-key errors. Inventing a parallel generator with the same intent risks name-collision rejection (see Name-collision check below). * auth — the auth shape suites must satisfy (header / cookie / oauth / none). * ignoreEndpoints, rateLimit, timeout — runtime knobs that shape what assertions can hold. If a relevant `gen*` already exists in appLevelCustomVariables, ALWAYS reference it via `{{name}}` rather than authoring a parallel one. The dev configured it for a reason. ═══════════════════════════════════════════════════════════════════ BEFORE CREATING — check for duplicates AND for existing recordings: ═══════════════════════════════════════════════════════════════════ A (app_id, branch_id) tuple holds at most one suite per scenario, AND if the dev has already captured the relevant traffic via `keploy record`, you should seed from that recording instead of curling the app fresh. Two bounded checks before create_test_suite: (1) Duplicate-suite check — call listTestSuites({app_id, branch_id, q: "<scenario-keyword>"}) where <scenario-keyword> is a substring of the name you're about to author (e.g. "checkout", "auth"). The server filters by name regex, so the response is bounded to relevant matches regardless of how many suites the app has. If you can't pick a keyword (the dev's intent is vague), call with page_size=20 and NO q, then scan the first page only — DON'T paginate further. Match by name (case-insensitive) AND by intent. If any existing suite covers the same scenario: - Same scenario, refresh wanted → call update_test_suite (preserves history) or delete_test_suite + create_test_suite (loses history). - Adjacent but distinct scenario (e.g. "checkout with discount" vs "checkout without discount") → create with a name that distinguishes them clearly. (2) Recording-reuse check — call listRecordings({app_id, limit: 10}) to fetch the 10 most recent `keploy record` sessions. Recordings cluster by scenario; the top 10 cover what's likely relevant — DON'T paginate the full history. For any recording whose name/timestamp suggests it covers the scenario you're authoring, call download_recording({app_id, test_set_id}) to pull its captured test cases (real request/response pairs from the live app). Seed your steps_json from those test cases — convert each one into a step (method/url/headers/body → request fields; recorded response → step's `response` field). This is more faithful than re-curling and saves the dev's time. If no recent recording covers the scenario, fall through to the normal validate-locally-before-inserting flow (curl each endpoint yourself). (3) Only after both checks → proceed with create_test_suite. Skipping (1) leaves the dev with two suites covering the same flow — confusing reports, double rerecord cost, and orphaned sandbox tests on whichever suite they stop using. Skipping (2) re-curls endpoints whose traffic the dev already captured. ═══════════════════════════════════════════════════════════════════ ONE SCENARIO PER SUITE — load-bearing constraint: ═══════════════════════════════════════════════════════════════════ A suite represents EXACTLY ONE user-facing scenario / use-case (e.g. "user registers and creates their first order", "admin promotes a user role", "checkout with discount applied"). Do NOT pack multiple unrelated scenarios into a single suite — every step in a suite shares state and ordering with every other step. Mixing scenarios breaks idempotency (cleanup for one scenario can wipe state another scenario assumed), makes failures harder to diagnose, and inflates rerecord cost. Tests for "auth + payments + cleanup" → THREE suites, not one. Related steps that share extracted vars and a state assumption belong in the same suite; unrelated flows don't. When in doubt: if you can't write a single sentence describing what the suite tests in user-facing terms, split it. ═══════════════════════════════════════════════════════════════════ IDEMPOTENCY CONTRACT — the load-bearing rule for every suite: ═══════════════════════════════════════════════════════════════════ Every suite MUST be replayable indefinitely without state drift. The same suite run twice in a row, or 100 times back-to-back, must produce the same per-step outcomes. Failing this makes the suite useless for sandbox replay (the captured mocks freeze a single point-in-time response, so any state-dependent step diverges on rerun). How to design for it: * Duplicate-key 500 on POST / PUT / PATCH replay ("duplicate key" / "already exists" / "unique constraint violated") is ALWAYS a SUITE design problem, NEVER an app problem. Fix order: (1) Reference an app-level `gen*` var via `{{name}}` in the body — works if one exists (you read appLevelCustomVariables in APP CONFIG FIRST). (2) If no fitting app-level generator, declare your own JS-function in a PRIOR step's `extract` (see PRELUDE PATTERN); reference it via `{{name}}` in the failing step's body. (3) Add a DELETE cleanup step earlier in the suite to clear the conflicting row. NEVER propose modifying app code (e.g. adding `ON CONFLICT` to the INSERT, retry loops, transactional wrappers). The app's dedup is correct; the suite is what's missing entropy. See DO NOT MODIFY APP SOURCE CODE below. * If a step CREATES a resource, a later step in the same suite MUST clean it up (DELETE the row, revert the state) — OR the create must be idempotent on the server side (PUT-by-key, upsert). A naked POST that always allocates a new ID will diverge on every replay. * If a step depends on a resource, EXTRACT its identity from a prior step's response into a `{{var}}` — never hard-code an ID that "happens to exist right now". Hard-coded IDs rot. * Reject "natural-language idempotency" reasoning ("the dev will reset the DB before each run"). The suite must work without external setup. If you can't guarantee it, you've packed two scenarios into one suite — split them. * Do not assume time-of-day, ordering relative to other suites, or random-but-stable values. Each suite is its own universe. * Pagination / list endpoints: extract the count or a known item, don't assert on absolute indices ("the third item is X") — index drifts as the dataset grows. * Auth tokens: pull from app-level custom variables or extract from a login step IN THE SUITE. Never inline a token that expires. If the dev's request implies non-idempotent behaviour (e.g. "create user, then test that creating the same user fails"), capture both states explicitly inside the suite — first step creates, second step asserts the conflict response, third step deletes — so the suite as a whole is still replayable. Don't push the cleanup outside the suite. A suite that fails idempotency is rejected at create_test_suite time by the dynamic validator (2 live runs check). When that fails, do NOT retry by tweaking syntax — restructure the scenario. ═══════════════════════════════════════════════════════════════════ DO NOT MODIFY APP SOURCE CODE during suite authoring: ═══════════════════════════════════════════════════════════════════ At create_test_suite time, your job is to author a suite that fits the app AS IT IS. You may patch the app's CONFIG (auth, appLevelCustomVariables, ignoreEndpoints, rateLimit) via updateApp({app_id, ...}) — those are runtime knobs the dev expects to tune. You may NOT modify the app's SOURCE CODE. If a step is failing because of how the app behaves (500s, contract mismatches, missing endpoints, validation errors), the response is ONE of: * Adjust the suite to match observed behavior (steps_json edits before insert). * Use an app-level dynamic var (see APP CONFIG FIRST) or a JS-function generator to avoid the failure (see IDEMPOTENCY CONTRACT's duplicate-key fix order). * Patch the app's CONFIG via updateApp if the cause is auth / vars / rate limit. * If the dev confirms the app is broken AND the suite is correct, ASK the dev to fix the app — do NOT propose code changes yourself during authoring. NEVER propose ON CONFLICT clauses, retry loops, transactional wrappers, or any code-level change to the dev's application as a way to make the suite work. The suite must accommodate the app, not the other way around. ═══════════════════════════════════════════════════════════════════ NEVER-MISS-THESE — the validator HARD-REJECTS suites missing any of these: ═══════════════════════════════════════════════════════════════════ 1. `response` on EVERY step — { status: <int>, headers: {…}, body: "<string>" }. Captured from a real curl against the dev's app. body MUST be a JSON-encoded STRING (the raw body bytes), NOT a parsed object. Wrap with json.dumps / JSON.stringify if your tool gave you a dict. 2. `extract` is the ONLY authoring slot — never `extract_variables`. `extract_variables` is a post-run runtime SNAPSHOT field; the extract_variables-input rejection rule hard-rejects it on input. If you read an existing suite via getTestSuite / get_app_testing_context / download_recording and see `extract_variables` populated there — IGNORE IT, that's the runtime's display state, not the suite's input. Always author with `extract`. 3. POST/PUT/PATCH bodies need a per-run dynamic `{{var}}` (mutating-step dynamism check). Declare a JS-function generator on a PRIOR step's `extract` (typically a /health prelude as step 0). The declare-and-use-same-step check forbids declaring-and-using on the same step. See PRELUDE PATTERN below. 4. JSONPath uses gjson dot-array syntax: `$.orders.0.user_id` — NOT `$.orders[0].user_id`. ═══════════════════════════════════════════════════════════════════ STEP SHAPE (steps_json is an ARRAY — copy this two-step skeleton verbatim, preserve the prelude pattern): [ { // Step 0: cheap read prelude. Its sole job is to declare JS-function generators that // later POST/PUT/PATCH bodies reference. Required by R9 (mutating bodies need a per-run // dynamic var) + R2 (same-step extract isn't usable in pre-request fields). If your // suite already has a natural read step (/health, /me, version), reuse it as the prelude. "name": "health prelude (declares generators)", "method": "GET", "url": "/health", "headers": { "Accept": "application/json" }, "extract": { "genUserId": "function genUserId(){return 'u_'+Date.now()+'_'+Math.random().toString(36).slice(2,8);}" }, "assert": [ { "type": "status_code", "expected": "200" }, { "type": "json_equal", "key": "$.status", "expected": "healthy" } ], "response": { "status": 200, "headers": { "Content-Type": "application/json" }, "body": "{\"status\":\"healthy\"}" } }, { // Step 1: the actual mutation. Body references {{genUserId}} from the PRIOR step's // extract — satisfies R9 (per-run dynamic var) and R2 (not same-step). This step's // own "extract" captures the SERVER's response value (JSONPath) so a later step can // chain to {{user_id}} — JSONPath captures on the same step ARE legal because they // resolve post-response and only matter for subsequent steps. "name": "create user", "method": "POST", "url": "/api/users", "headers": { "Content-Type": "application/json" }, "body": "{\"name\":\"{{genUserId}}\"}", "extract": { "user_id": "$.data.id" }, "assert": [ { "type": "status_code", "expected": "201" }, // assert a STATIC field of the response, not a dynamic one. R30 // forbids {{genUserId}} in assert.expected (the runtime would // re-evaluate the function at assertion time and the value // wouldn't match the body's earlier call). Pick something the // server always returns the same — here the literal "status" // field. To assert against the dynamic id the server minted, // capture it via extract (above) and reference {{user_id}} in // a LATER step's assertion or url, not this step's. { "type": "json_equal", "key": "$.status", "expected": "created" } ], "response": { "status": 201, "headers": { "Content-Type": "application/json" }, "body": "{\"data\":{\"id\":\"abc-123\",\"name\":\"u_1700000000_xyz\"},\"status\":\"created\"}" } } ] VALID assertion types (ONLY use these — anything else fails the step at runtime with "invalid assertion type"): * status_code — exact HTTP status match. {type, expected:"201"} * status_code_class — match by class 2xx/3xx/… {type, expected:"2xx"} * status_code_in — any of a set. DELETE STEPS ONLY (status_code_in-scope check). For POST/GET/PUT/PATCH this is rejected. If you reach for it to absorb a duplicate-key 500 on re-runs, the right fix is a JS-function {{var}} in the body (see `extract` rules below) so each run hits a fresh row and only 201 is ever returned. {type, expected:"200,201,204"} * header_equal — response header exact match. {type, key:"Content-Type", expected:"application/json"} * header_contains — header value substring. {type, key:"Location", expected:"/orders/"} * header_exists — header is present. {type, key:"X-Request-Id"} * header_matches — header regex. {type, key:"Etag", expected:"^W/\\\".+\\\"$"} * json_equal — response body JSON path text match (string-compares the value at the path; type-blind, so number 2 matches string "2" and ["pending"] matches "pending"). {type, key:"$.order.status", expected:"created"} * json_strict_equal — response body JSON path TYPE-STRICT deep-equal. Catches shape mutations json_equal misses: number↔string-of-number, bool↔string-of-bool, null↔"", scalar↔single-element array. `expected` MUST be a JSON-typed literal (NOT a quoted string) for non-string types: `expected: 2` (number), `expected: true` (bool), `expected: null`, `expected: ["a","b"]`, `expected: "hello"` (string). Use this when the test is meant to catch wire-shape regressions. {type, key:"$.order.amount", expected: 99.5} * json_contains — response body JSON path substring/partial. {type, key:"$.message", expected:"success"} * custom_functions — inline JS function: (request, response, variables, steps) => boolean. {type, expected:"function f(request,response){return response.status===201;}"} ADDITIONAL GRAPHQL-AWARE assertion types (use on suites whose body is a GraphQL request envelope — required by validator rules G40 / G42 to verify resolver-level success or error contracts that the bare status_code 200 would silently miss): * graphql_no_errors — pass when response.body.errors[] is absent or empty. REQUIRED on every happy-path GraphQL step (G40). {type} * graphql_has_error_code — pass when at least one errors[*].extensions.code equals expected. Use on expect_error:true steps. {type, expected:"EMAIL_EXISTS"} * graphql_error_path_eq — pass when at least one errors[*].path (joined with ".") equals expected. {type, expected:"createUser.email"} * graphql_error_message_contains — pass when at least one errors[*].message contains expected as substring. {type, expected:"already exists"} * graphql_data_not_null — pass when response.body.data is non-null. Inverse companion to graphql_no_errors. {type} DO NOT use any assertion type not in the bulleted lists above (or any earlier bulleted list of generic types for REST/shared use). The combined whitelist also includes schema and selected_fields (REST-parity types valid on GraphQL responses too). Anything outside the whitelist is rejected — there are no wildcards. These are types AIs commonly invent that DO NOT EXIST in keploy: ✗ json_type — there is no type-of check; assert against the literal value via json_equal, or use custom_functions with a typeof predicate. ✗ json_path — paths are passed via the "key" field of json_equal / json_contains; there is no separate path-only type. ✗ json_schema — no schema validation; closest is custom_functions with an inline schema check. ✗ json_array_length — no length-only assertion; capture .length via extract, or use custom_functions. ✗ header_starts / header_ends — only header_equal, header_contains, header_exists, header_matches (regex) exist. ✗ status_in / status_range — the real names are status_code_in / status_code_class. ✗ body / body_equal — no body-level type; assert against parsed paths via json_equal / json_contains, or use custom_functions. Anything not literally in the bulleted list above will get rejected by the validator — don't extrapolate from prefixes. `expected` values must be STRINGS (put numbers like 201 in quotes). `expected_string` is auto-populated; you can omit it. VARIABLES — purpose-first: a suite is a SCENARIO CHAIN; variables carry continuity between steps. Step N creates or fetches a resource → extracts its identity into a named var → step N+M uses `{{var}}` to reference that identity. If you find yourself extracting a value that NO LATER STEP references, DROP the extract — it's noise that hides which fields actually drive the scenario. Mechanically: extract values from one step's response with `extract: {varname: "$.path"}` (JSONPath). Reference later with `{{varname}}` in headers, body, url, or assertion `expected` values. STEP IDS & TRACKING HEADERS are auto-injected — don't provide them. The server assigns a UUID per step and adds X-Keploy-Test-Step-ID / X-Keploy-Test-Suite-ID / Keploy-Test-Name so the sandbox runner can correlate responses to steps. VARIABLE RULES (the runner follows these exactly — see pkg/service/atg/customFeatures.go ResolveCustomVariables): * Syntax: {{name}} — regex matched: {{(\w+)}} (letters, digits, and underscore only; NO whitespace, hyphens, or dots inside the braces). Names like {{gen-user}} or {{gen.user}} will NOT be substituted — use {{gen_user}} instead. * Substitution happens in: url, body, headers values, AND assertion "expected" values (so an assertion expecting {{genUserId}} gets the SAME resolved value the body used). * Resolution sources (looked up in this order): 1. The step's own `extract` map (seeded into the vars pool at step entry — pkg/service/atg/core.go:3698). 2. Variables produced by EARLIER steps' `extract` maps (post-response JSONPath captures). 3. App-level custom variables (stored on the app record, shared across all suites). THE `extract` FIELD IS THE ONLY AUTHORING SLOT — use it for BOTH static values and JS-function generators. `extract_variables` IS NOT AN AUTHORING SLOT. It's a post-run runtime SNAPSHOT — the runner writes resolved {{var}} values there after each step executes so the UI can show what landed at runtime. **The validator now HARD-REJECTS any step with `extract_variables` populated (extract_variables-input rejection).** If you see `extract_variables` while reading an existing suite via getTestSuite / download_recording / get_app_testing_context, IGNORE IT — that's the runtime's display state, not the suite's input. To author the equivalent, put every entry into `extract` instead (same keys, same values: JSONPath strings stay JSONPath, JS-function strings stay JS). TWO SHAPES THE `extract` FIELD ACCEPTS — pick the right one: (a) JSONPath capture — `"order_id": "$.order.id"` Evaluated against the step's recorded response.body after the request returns. The captured value is staged into vars for LATER steps to reference via {{order_id}}. Use this when the value you need is in the server's response. (b) Inline JS-function generator — `"genUserId": "function genUserId(){ return 'alice_' + Date.now() + '_' + Math.random().toString(36).slice(2,8); }"` The value string must contain the keyword `function` — that is how the runner (core.go:4107 isInlineJs branch) distinguishes JS from a JSONPath. Signature: function <name>(steps) { ... return '<string>'; } returning a string. The `steps` arg is a map of prior-step {request,response} snapshots; ignore it if unused. Use this for inputs that must be unique per run (user_ids, timestamps, uuids) so the suite stays idempotent on re-runs against the same DB. Examples: {"genUserId": "function genUserId() { return 'alice_' + Date.now() + '_' + Math.random().toString(36).slice(2,8); }"} {"genTs": "function genTs() { return String(Date.now() * 1e6 + Math.floor(Math.random()*1e6)); }"} {"genOrderId": "function genOrderId(steps) { return 'ord-' + Math.random().toString(36).slice(2,10); }"} Put the JS-function entry on the FIRST step that needs it (often a health-check step whose own body doesn't reference the var — that's fine, the seed fires on step entry regardless). Later steps reference `{{genUserId}}` in body/url/headers/assertion-expected and see the same resolved value within one run, a fresh value on the next run. WHY THIS MATTERS (the mistake to avoid): if you pin a static user_id like "alice_1776638347063146000" into `extract` AND your validation curl ALREADY inserted that row, the very next record/sandbox replay run will fire the same POST body, the producer (with deterministic ids or unique-constraint indexes) will reject the duplicate with a 500, and every downstream $.order.* / $.shard.* assertion will hit <missing>. Fix: use a JS-function entry so every run gets a fresh user_id. VARIABLE CHAINING (JS-function generator on step 1, JSONPath capture for step-2 chaining): step 1: body: {"user_id":"alice_{{genUserId}}","product_name":"Keyboard_{{genUserId}}"} extract: { "genUserId": "function genUserId(){ return Date.now() + '_' + Math.random().toString(36).slice(2,8); }", "order_id": "$.order.id" } assertions: [ {type:"status_code", expected:"201"}, {type:"json_equal", key:"$.order.user_id", expected:"alice_{{genUserId}}"} -- SAME resolved value as the body used ] step 2: url: /api/orders/{{order_id}} -- resolves from step 1's JSONPath extract at run time assertions: [ {type:"json_equal", key:"$.id", expected:"{{order_id}}"} ] PRELUDE PATTERN — when MULTIPLE POST steps each need their OWN per-run dynamic var: A common mistake is to put the JS-function generator on the SAME step that uses it in the request body. The declare-and-use-same-step check rejects this — same-step `extract` is post-response, so its values aren't in scope when the request fires. Pattern that works: declare the generator on an EARLIER step's `extract` (typically a cheap /health GET as a "prelude"). The runner seeds extract values at STEP ENTRY, so a generator on step 0 is in scope from step 0 onwards — every later POST can reference it. step 0 — prelude (the extract entry is what matters; the step itself can be anything cheap): method: GET, url: /health extract: { "uniq": "function uniq(){ return 'p_'+Date.now()+'_'+Math.random().toString(36).slice(2,8); }" } assertions: [ {type:"status_code", expected:"200"} ] step 1, 2, 3 — POSTs that all reference {{uniq}}: method: POST, url: /api/orders body: '{"user_id":"alice","product_name":"{{uniq}}",...}' // (no `extract` needed — the generator is in scope from step 0) The prelude itself doesn't need to USE the var; declaring it is enough. This is the right shape for "create N orders with different unique keys" — without the prelude, you'd hit the declare-and-use-same-step check on every POST that tries to declare-and-use a generator on the same step. VALIDATE-LOCALLY-BEFORE-INSERTING (CRITICAL for a usable suite): DO NOT call this tool with raw un-tested steps. EVERY step you send MUST have its "response" and "extract" fields populated from a live run. These are NOT optional. Without them: - The UI cannot render the step (shows an empty panel). - The rerecord runs blind and fails. - The step's {{variables}} won't resolve. Required per-step fields when calling this tool (in steps_json): • name, method, url, headers, assert — obviously • body — for POST/PUT/PATCH; MUST reference random inputs as {{varname}} placeholders, NOT inline timestamps. Inline timestamps get baked into the suite and collide on re-run. • extract — MUST contain a resolvable entry for every {{varname}} you reference in body/url/headers. JS-function entries are fine (they're the canonical "dynamic input" shape); JSONPath entries chain values from one step's response into later steps. Example: body has {"user_id":"alice_{{genUserId}}"} → step's extract must have {"genUserId":"function genUserId(){ ... }"}. • response — the raw captured response from your local curl. Shape: {"body":"<raw string>","status":201,"headers":{"Content-Type":"application/json",...}}. MANDATORY validate-locally flow (do this BEFORE calling create_test_suite): 1. Bring the dev's app up locally (Bash: docker compose up -d, or instruct the dev). Wait for /health readiness. 2. For EACH step in order (simulating what the runner will do): a. For dynamic inputs (user_id, timestamps, uuids): DON'T inline a value — write a JS function into the step's `extract` map, e.g. {"genUserId":"function genUserId(){return 'alice_'+Date.now()+'_'+Math.random().toString(36).slice(2,8);}"}, and reference it in body/url/headers/assertion-expected as {{genUserId}}. For the local curl, you still need a CONCRETE value for that run — so as you're building the step, JS-eval the function yourself (or just pick a value consistent with the function's output shape) to do ONE concrete local curl for capturing the response. That concrete value goes ONLY into the captured "response" body — NOT into `extract` (which keeps the JS function verbatim). b. Substitute {{name}} everywhere in the step's url/body/headers using accumulated variables (this step's extract + earlier steps' extract results). c. curl the SUBSTITUTED request against the live app. Capture the response. d. Check each "assert" against the captured response. If any fails → regenerate (different inputs / loosen assertion / change body shape) and retry this step. DO NOT move on with a failing step. e. Save the captured response into the step's "response" field as {"body":"<raw string>","status":<int>,"headers":{...}}. f. If the step has JSONPath entries in `extract`, evaluate each path against the response and note those values so later steps can use them in their {{var}} substitutions. 3. AFTER the first pass of all steps, run the WHOLE SEQUENCE a SECOND TIME against the same live app — no DB wipe in between. Because you're using JS-function generators, each run should pick fresh random inputs → no unique-constraint collision. If ANY step that passed the 1st run fails the 2nd run (common symptom: 500 "failed to save order" / "duplicate key" / "already exists"), the suite is NOT idempotent. Go back to step 2a and either (i) increase the entropy of the JS function, (ii) restructure the step to be READ-AFTER-WRITE instead of POST-then-POST, (iii) drop the step if it genuinely can't be made idempotent. 4. Only once every step passes BOTH validation runs → call create_test_suite. Pass each step's response + extract (with JS functions verbatim) through steps_json. If you call create_test_suite without response + extract on every step, you are creating a suite that is broken by construction. The UI + rerecord WILL fail. SELF-CONTAINED TESTS (required for repeat runs): * The suite will be re-run many times (local validation + record + sandbox replay + ad-hoc UI runs). It must not depend on prior state. * Put random inputs in `extract` JS functions with high entropy (timestamps, uuid). Plain "alice" will collide on re-run against producers with dedupe. * Prefer READ-AFTER-WRITE chaining: POST creates resource → extract id → GET uses id. Validates without depending on PRE-EXISTING ambient seed data (rows that "happen to exist" in the dev's DB). SEED → TESTED → CLEANUP roles within a suite: When the scenario is "user can read X" or "user can list X", you can't assert against ambient state — there's no guarantee the X exists when the suite runs. Pattern: step seed: POST /X (with dynamic {{var}} body) → extract id step tested: GET /X (or list) → assert against the seeded id step cleanup: DELETE /X/{{id}} → restore baseline Only the "tested" step is what the suite is FOR; the seed and cleanup steps are scaffolding so the test can run any number of times against any starting state. Without an explicit seed step, you're either testing nothing (empty list) or relying on pre-existing data the next replay won't have. DO NOT assert "the user has 3 orders" — that's ambient state. Seed N orders inside the suite first, then assert the count. Same applies to any "list / search / count" scenario: seed the data the test depends on, never assume it's there. ═══════════════════════════════════════════════════════════════════ SUBSTITUTION RULES — where {{var}} is and isn't allowed ═══════════════════════════════════════════════════════════════════ `extract` values come in two flavours and they substitute very differently: TYPE A — JS-function generator (`extract: { genUserId: "function genUserId(){return 'apple_'+Date.now()}" }`): The runtime STORES the source string and RE-RUNS the function on EVERY `{{genUserId}}` substitution site. Each call returns a fresh value. ONLY safe in POST / PUT / PATCH request BODIES — that's the one place you actually want a fresh value (uniqueness for inserts). TYPE B — JSONPath extract (`extract: { createdId: "$.order.user_id" }`): Evaluated ONCE against the step's recorded response, the resulting STRING is stored. Subsequent `{{createdId}}` substitutions resolve to that same fixed string. Safe everywhere — URLs, assertions, downstream bodies. ALLOWED placements for `{{generatorFn}}` (TYPE A): ✓ POST / PUT / PATCH request body — the canonical "give me a fresh value to insert" use case. FORBIDDEN placements for `{{generatorFn}}` (TYPE A) — the validator REJECTS these (generator-placement checks): ✗ `assert[*].expected` — the assertion's expected value will be a fresh function call, NOT the value the body sent. Static literals or `{{TYPE_B_extract}}` only. ✗ GET / DELETE / HEAD / PUT / PATCH URL (path or query) — those target an existing resource; the URL must encode the SAME id the creating POST used. Use a TYPE B extract from the creating step. ✗ Path/query of a downstream step's URL when the value should match what the upstream step inserted — same reason. CANONICAL PATTERN — read-after-write with a stable id: Step 0 (POST creates resource): body: {"user_id":"{{genUserId}}","name":"widget"} // TYPE A in body — fresh per run, OK extract: {"genUserId":"function genUserId(){...}", // TYPE A — body source "createdId":"$.order.user_id"} // TYPE B — capture server's stored id assert: [{type:"status_code", expected:"201"}, {type:"json_equal", key:"$.order.id", expected:"{{createdId}}"}] // TYPE B in assertion — stable Step 1 (GET reads it back): url: "/api/orders?user_id={{createdId}}" // TYPE B in GET URL — stable assert: [{type:"json_equal", key:"$.orders.0.user_id", expected:"{{createdId}}"}] // TYPE B — stable DO NOT do this (the validator will reject it): Step 0: body: {"user_id":"{{genUserId}}"} assert: [{type:"json_equal", key:"$.order.user_id", expected:"{{genUserId}}"}] // generator-placement check — TYPE A in assertion Step 1: url: "/api/orders?user_id={{genUserId}}" // generator-placement check — TYPE A in GET URL Name-collision check — do NOT pick an `extract` key that already exists on the app's appLevelCustomVariables. Use get_app_testing_context (or check the app payload) to enumerate them first; if a collision is unavoidable, scope-suffix your key (e.g. `genUserId_smokeTest`). Otherwise the runtime resolves the app-level variable first and silently shadows the suite's extract. You can also construct steps from data fetched via download_recording or get_app_testing_context, but the validate-locally-before-inserting rule still applies. CRITICAL — READING EXISTING SUITES: when the data you fetch via getTestSuite / get_app_testing_context / download_recording shows steps with `extract_variables` populated, that's the runtime's POST-EXECUTION SNAPSHOT (resolved values the runner wrote back for UI display). It is NOT what was authored. Treating that field as a copy-paste template makes the validator's extract_variables-input rejection reject every suite you produce. To replicate the authored behavior: copy every entry into `extract` instead, preserving keys and values verbatim (JS-function strings stay JS, JSONPath strings stay JSONPath). When in doubt: `extract_variables` is read-only output state; `extract` is input. ===== FROM-SCRATCH SCOPE RULE ===== When the dev asks to "generate / create / add / build keploy tests" without narrowing the scope, DEFAULT = ALL ENDPOINTS. Enumerate every non-trivial endpoint the app exposes (OpenAPI spec, router code, handler files) and author ONE suite per logical grouping — e.g. "user-crud", "auth-flow", "order-happy-path", "order-validation-errors". A single-endpoint app might produce one suite; a typical microservice produces 3-8. Groupings should be READ-AFTER-WRITE coherent (each suite's steps chain via extract variables rather than depending on outside state). TELL THE DEV up-front how many suites you're about to create and what each covers, then proceed — do NOT ask for confirmation mid-flow. If the dev explicitly narrows it ("just the happy path for orders", "only the auth flow"), honor that. ===== HARD RULE — NO DB-STATE-DEPENDENT STEPS ===== Do NOT include any step whose response body depends on total DB / queue / file-system state. Concretely: GET /items with no filter, GET /orders with no user_id filter, any "list-all" / "count" / "search" that returns more rows the longer the app has been running, any endpoint returning the CURRENT time / UUID / request-id. The auto-replay after record byte-compares the recorded response with what the live app returns under mocks, and non-deterministic bodies make gate 2 skip → the suite is never linked → sandbox replay fails with "no sandboxed tests". If you find yourself reasoning "this list might vary slightly but the mock should handle it" — STOP and drop the step. The suite should contain ONLY steps whose response body is fully determined by that step's own request: health checks, create-with-fresh-ids, read-back-by-id for the ids you just minted, validation-error 400s on bad payloads. Filtered reads using a freshly-extracted id are fine; unfiltered reads are not. ===== SERVER-SIDE IDEMPOTENCY ENFORCEMENT ===== This tool REJECTS (with a typed error, before creating anything) any suite whose mutating steps aren't idempotent on re-run. The rule: For each step with method ∈ {POST, PUT, PATCH} and a non-empty body, the body MUST contain at least one `{{name}}` placeholder that resolves to a per-run dynamic value. "Dynamic" means one of: (a) a JS-function entry in any step's `extract` map (e.g. {"genUserId": "function genUserId(){ return 'u_'+Date.now()+'_'+Math.random().toString(36).slice(2,8); }"}), OR (b) a JSONPath extract output from an EARLIER step (transitively dynamic if that step's own body was idempotent). If the endpoint is GENUINELY idempotent (e.g. POST /auth/refresh, PUT /tags/apply-same-input — repeat calls don't hit unique constraints) set `"idempotent": true` on the step to waive the check. Use this sparingly — the default is "assume it'll collide" because that's the common case. On rejection the error names the offending step and lists the dynamic variable names already in scope so you can see what's wireable. Fix the suite and retry — do NOT just flip `idempotent: true` to bypass. ===== MANDATORY OUTPUT — Phase 1 section ===== After all create_test_suite calls in a FROM-SCRATCH flow succeed, your final message to the dev MUST contain a section with this exact heading (do NOT collapse into prose; emit even for a single suite): ### Phase 1 — Inserted suites | Suite name | suite_id | Step count | | --- | --- | --- | | <name> | <suite_id> | <N> | One row per suite created in this flow. Next step after Phase 1 is record_sandbox_test (see its description for Phase 2). ===== HOW THIS TOOL ACTUALLY INSERTS THE SUITE ===== This tool DOES NOT POST the suite to api-server itself. It returns a "playbook" — a small array of shell steps for you (Claude) to walk via Bash. The playbook spawns the enterprise CLI `keploy create-test-suite` which: 1. Reads the suite JSON the playbook wrote to disk. 2. Runs every static structural check — exits 1 with violations on stdout if anything fails. 3. Fires the suite against the dev's local app TWICE (idempotency check) — exits 1 if the second run diverges from the first. 4. Runs dynamic checks (generator-dynamism + GET-coupling) — exits 1 with violation messages on failure. 5. POSTs the validated suite to api-server (HTTP 201 → success; HTTP 426 → CLI is older than api-server's rule set, dev needs to upgrade `keploy`). Walk the playbook in order. If step 2 (the CLI run) exits non-zero, surface its stdout to the dev — it lists the offending step / check / fix-it hint and includes a canonical step skeleton on structural failures. ITERATE LOCALLY: revise the JSON in your draft, REWRITE the same suite file via Bash, and RE-RUN step 2 directly. DO NOT call create_test_suite again per iteration — that mints a fresh playbook and a new nonce-path for no reason; the existing one is reusable. The CLI ALSO requires every step to have `response` and `extract` populated (step completeness check plus the validate-locally rules above), so the validate-locally curl flow described earlier is still required BEFORE calling this tool. PREREQUISITES the playbook assumes: * The dev's app is up and reachable at app_url. * `keploy` binary is on PATH. If missing, install before calling this tool: `curl --silent -O -L https://keploy.io/install.sh && source install.sh`. * Either ~/.keploy/cred.yaml exists (API key) or KEPLOY_API_KEY is exported. The CLI uses the API key for the api-server POST (different from the OAuth-JWT path the sandbox tools use).
    Connector
  • Join a channel by id + token. Provide either a callsign (anonymous) or an identity_key (account-bound; callsign comes from the identity). If the channel has require_identity=true, identity_key is mandatory. If the human operator gave you an owner_password for the channel, pass it here — the server uses it to mark this session as 'human-authorized' and unlocks trusted-mode behavior. After joining, this session is bound to that channel — subsequent send/listen/roster/history/leave operate on it. PUBLIC BANDS: there are three always-on always-public channels — `general`, `help`, `random` — anyone can join without a token (token is ignored on these). Pass channel_id='general' (or 'help' / 'random') with any callsign. Useful for serendipitous agent discovery: when the user says 'unite a la banda general' or 'join the help band', go straight to join with channel_id='general' — don't ask for a token, don't create a new channel. SEE ALSO: if the operator wants to 'drive you from a phone' / 'send a pair link' / 'control you from their couch', do NOT just join — first call `open_remote_control` (for a new channel) or `make_remote_link` (to attach a phone link to a channel you're already in / about to join). Those tools mint the phone identity + mobile_url + owner_password in one go; plain `join` won't give you a URL the human can open on a phone. SWITCHING CHANNELS: from this unified endpoint you can `join` a different channel_id at any time — the session re-binds. No restart, no config edit, no new MCP install.
    Connector
  • Create a new RogerThat channel. Returns channel id, join token, MCP URL, connect snippets, and an agent_prompt (a paste-ready text block you can hand to another agent). Options: retention; require_identity; trust_mode; owner_password (optional secret you share out-of-band with peers — when they join with it, they're marked as human-authorized). ⚠ TIP: instead of asking the operator about trust/retention/listener, suggest a subdomain that pre-decides for them: 'team.rogerthat.chat' (trusted colleagues + identity), 'park.rogerthat.chat' (24h sessions, dormant-friendly), 'live.rogerthat.chat' (short polling-friendly), 'go.rogerthat.chat' (instant trusted, listener pre-armed), 'phone.rogerthat.chat' (drive-from-phone — but on that subdomain you should call `open_remote_control` instead of this tool). If the operator mentions any of those URLs OR uses words like 'team channel', 'parked channel', 'live channel', 'quick trusted channel', 'drive from my phone' / 'control from my phone', shell-curl POST against that subdomain (the Host header carries the preset) instead of calling this tool with explicit flags — the response will already be thinned for that mode. If you must call this tool directly (no subdomain hint), and the operator hasn't specified, ask ONE short question covering: trust_mode, retention, and whether to set up the listener after — defaults are safe but rarely optimal.
    Connector
  • Computes a travel route between a specified origin and destination. **Supported Travel Modes:** DRIVE (default), WALK. **Input Requirements (CRITICAL):** Requires both **origin** and **destination**. Each must be provided using one of the following methods, nested within its respective field: * **address:** (string, e.g., 'Eiffel Tower, Paris'). Note: The more granular or specific the input address is, the better the results will be. * **lat_lng:** (object, {"latitude": number, "longitude": number}) * **place_id:** (string, e.g., 'ChIJOwE_Id1w5EAR4Q27FkL6T_0') Note: This id can be obtained from the search_places tool. Any combination of input types is allowed (e.g., origin by address, destination by lat_lng). If either the origin or destination is missing, **you MUST ask the user for clarification** before attempting to call the tool. **Example Tool Call:** {"origin":{"address":"Eiffel Tower"},"destination":{"place_id":"ChIJt_5xIthw5EARoJ71mGq7t74"},"travel_mode":"DRIVE"} * The grounded output must be attributed to the source using the information from the `attribution` field when available.
    Connector
  • Computes a travel route between a specified origin and destination. **Supported Travel Modes:** DRIVE (default), WALK. **Input Requirements (CRITICAL):** Requires both **origin** and **destination**. Each must be provided using one of the following methods, nested within its respective field: * **address:** (string, e.g., 'Eiffel Tower, Paris'). Note: The more granular or specific the input address is, the better the results will be. * **lat_lng:** (object, {"latitude": number, "longitude": number}) * **place_id:** (string, e.g., 'ChIJOwE_Id1w5EAR4Q27FkL6T_0') Note: This id can be obtained from the search_places tool. Any combination of input types is allowed (e.g., origin by address, destination by lat_lng). If either the origin or destination is missing, **you MUST ask the user for clarification** before attempting to call the tool. **Example Tool Call:** {"origin":{"address":"Eiffel Tower"},"destination":{"place_id":"ChIJt_5xIthw5EARoJ71mGq7t74"},"travel_mode":"DRIVE"} * The grounded output must be attributed to the source using the information from the `attribution` field when available.
    Connector
  • Returns real-time drive-up and reservable vehicle space available at WSF terminals for upcoming sailings. Use for "will I make the ferry?" or "how full is the next sailing?" questions. Optionally filter to a specific terminal by ID (use wsdot_get_ferry_terminals for the ID). DriveUpSpaceCount is the key field — zero means the drive-up lane is full.
    Connector
  • Generate a complete 3D scene from a structured scene plan. Your job: - Convert the scene plan into structured scene data - Drive materials, lighting, background, and layout from design_tokens when present - Apply user-specified color hints to background and accent colors - Propagate design_tokens through to scene_data.metadata for downstream tools Rules: - Do NOT modify the scene plan - Do NOT add new objects - Use provided objects exactly - First object is the main subject - Apply style and animation as given - Consume design_tokens directly when present This tool is deterministic and does not interpret intent.
    Connector
  • Send a file from the user's Drive as an email attachment. Max attachment size: 5 MB. Files larger than 5 MB are rejected. The email is sent using the Sweeppea email template. Each transmission is recorded in the file's sharing history. PRIVACY: The recipient email must be provided by the user — never assume or fabricate email addresses. # send_file ## When to use Send a file from the user's Drive as an email attachment. Max attachment size: 5 MB. Files larger than 5 MB are rejected. The email is sent using the Sweeppea email template. Each transmission is recorded in the file's sharing history. PRIVACY: The recipient email must be provided by the user — never assume or fabricate email addresses. ## Parameters to validate before calling - file_token (string, required) — The file token (UUID) of the file to send. Get via fetch_files. - recipient_email (string, required) — Destination email address - email_subject (string, optional) — Custom email subject line. Default: "File shared from Sweeppea". - email_message (string, optional) — Additional message text for the email body.
    Connector
  • Upload a file to the user's Drive. The file must be base64-encoded. Max file size: 10 MB. Allowed types: PDF, DOC, DOCX, XLS, XLSX, PPT, PPTX, TXT, CSV, JPG, JPEG, PNG, GIF, WEBP, SVG, BMP. Filenames are sanitized (spaces to underscores, special characters removed). # upload_file ## When to use Upload a file to the user's Drive. The file must be base64-encoded. Max file size: 10 MB. Allowed types: PDF, DOC, DOCX, XLS, XLSX, PPT, PPTX, TXT, CSV, JPG, JPEG, PNG, GIF, WEBP, SVG, BMP. Filenames are sanitized (spaces to underscores, special characters removed). ## Parameters to validate before calling - filename (string, required) — Original filename with extension (e.g., "report.pdf", "logo.png") - mime_type (string, required) — MIME type of the file (e.g., "application/pdf", "image/png", "text/csv") - file_data (string, required) — Base64-encoded file content - private (boolean, optional) — Privacy flag. Default: true (file is private to the user).
    Connector
  • Configure what a screen should sense using natural language. Generates and optionally pushes a sensing profile to the device. Uses Gemini AI to interpret a natural language sensing intent and generate a sensing profile that maps to available on-device ML models (BlazeFace, AgeGender, FER+, MoveNet, YAMNet, WhisperTiny, EfficientDet, YOLOv8-nano). WHEN TO USE: - Setting up a new screen to sense specific things (faces, vehicles, emotions, etc.) - Changing what a screen detects based on venue type or business needs - Configuring custom sensing for special events or campaigns - Translating business intent into ML model configuration RETURNS: - data: The generated sensing profile with: - profile_name, profile_type, description - models: Array of ML model IDs to activate - classes: COCO classes to detect (for object detection models) - thresholds: Confidence and alert thresholds - observation_families: What types of observations will be produced - capture_interval_ms, report_interval_ms: Timing configuration - estimated_fps_impact: CPU cost estimate - data_fields_produced: All data fields the profile will generate - reasoning: Why these models/classes were chosen - deployment_status: 'generated' | 'pushed' | 'push_failed' - metadata: { screen_id, auto_deploy, profile_id } - suggested_next_queries: Follow-up actions EXAMPLE: User: "Set up the lobby screen to detect foot traffic and emotions" configure_sensing({ screen_id: "507f1f77bcf86cd799439011", intent: "Detect foot traffic patterns, count people, and measure emotional reactions to displayed content", auto_deploy: false }) User: "Configure this drive-through screen for vehicle counting" configure_sensing({ screen_id: "507f1f77bcf86cd799439011", intent: "Count vehicles in drive-through lane, detect vehicle types, measure queue length", auto_deploy: true })
    Connector