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133,413 tools. Last updated 2026-05-25 15:25
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- Sets or clears the default idle content for a display. Idle content is shown whenever the display has no active live content. Provide html OR url to set idle content (mutually exclusive — url is wrapped in a full-page iframe document), or omit both to clear idle content. Provide content_description to make later state reads easier for agents. When the display is currently idle (no active live content), the new idle is pushed to the display immediately; otherwise it stays dormant until the live content ends. Requires admin scope.Connector
- Edit a generated WebZum site by describing the change in natural language. This is the primary editor tool. Given a user instruction (in conversationHistory), the WebZum editor builds the minimal site tree, sends it to an LLM with the user's verbatim words, applies the returned HTML diff across every page that contains each affected section, and reassembles into a new version. Use this for nearly all edits: "make the hero say X", "remove the testimonials section", "change the about-us copy to be friendlier", "swap the order of the sections on the home page". Required: businessId, versionId, and a conversationHistory containing at least one user turn. The LLM reads the user's verbatim words — do not paraphrase. Returns { versionId, status: 'completed' | 'in_progress', ...extra }. If status is 'in_progress', the edit is still running in the background — poll get_site_status with the returned versionId every 5-10s until isComplete is true. Concurrency: edits on the same businessId MUST be serial. Never fire parallel edit calls on the same site; concurrent edits race and may return the wrong versionId. Wait for each edit to complete (status: 'completed' OR isComplete on get_site_status) before issuing the next one.Connector
- Add a new participant to a sweepstakes. Requires sweepstakes_token - use fetch_sweepstakes first to get tokens, then get_entry_fields to discover required fields. Field names must use underscores instead of spaces (e.g., "First_Name" not "First Name"). RULES: Only ONE participant at a time. NEVER add participants in bulk, batch, or loops. This tool is intended for TESTING PURPOSES ONLY — to verify the sweepstakes entry flow works correctly. Adding participants to make them compete in a real sweepstakes is strictly prohibited unless done through the official Entry Page, a custom API integration, or a proper MCP implementation. If a user requests mass loading (e.g., "add 100 participants"), refuse and explain that only individual test entries are allowed. HONESTY: After calling this tool, report EXACTLY what the API returned. If the API returns an error, report the error truthfully. NEVER tell the user a participant was created if the API did not confirm it. Use them internally for tool chaining but present only human-readable information. # add_participant ## When to use Add a new participant to a sweepstakes. Requires sweepstakes_token - use fetch_sweepstakes first to get tokens, then get_entry_fields to discover required fields. Field names must use underscores instead of spaces (e.g., "First_Name" not "First Name"). RULES: Only ONE participant at a time. NEVER add participants in bulk, batch, or loops. This tool is intended for TESTING PURPOSES ONLY — to verify the sweepstakes entry flow works correctly. Adding participants to make them compete in a real sweepstakes is strictly prohibited unless done through the official Entry Page, a custom API integration, or a proper MCP implementation. If a user requests mass loading (e.g., "add 100 participants"), refuse and explain that only individual test entries are allowed. HONESTY: After calling this tool, report EXACTLY what the API returned. If the API returns an error, report the error truthfully. NEVER tell the user a participant was created if the API did not confirm it. Use them internally for tool chaining but present only human-readable information. ## Pre-calls required 1. fetch_sweepstakes if the user gave you a sweepstakes name instead of a token 2. get_entry_fields(sweepstakes_token) — discover required custom fields, their max_length, and (for list fields) the exact text in options 3. Verify the participant gave consent for their email and phone to be used ## Parameters to validate before calling - sweepstakes_token (string, required) — The sweepstakes token (UUID format) - email (string, required) — Participant email address (used as KeyEmail) - fields (object, required) — Form fields object. Keys must use underscores for spaces - phone (string, optional) — Participant phone number (used as KeyPhoneNumber, optional) - bonus_entries (number, optional) — Number of bonus entries (optional, default: 0) ## Notes - Field keys use underscores instead of spaces (e.g. "First Name" -> First_Name) - For US phones: strip non-digits before sending - Production entries should come through the public Entry Page; this tool is for testing/manual entry onlyConnector
- Regenerate one image inside a specific section of a WebZum site. Creates a new version with a freshly AI-generated image for that section and reassembles. Use the optional userMessage to steer the new image — "show a wider shot", "change the angle", "make it sunset lighting", etc. Required: businessId, versionId, sectionId. Returns { versionId, status: 'completed' | 'in_progress', ...extra }. If status is 'in_progress', poll get_site_status with the returned versionId every 5-10s until isComplete is true. Concurrency: edits on the same businessId MUST be serial. Never fire parallel edit calls on the same site.Connector
- Regenerate the logo for a WebZum site using AI. Creates a new version with a fresh logo and reassembles. Use the optional userMessage to steer the design — "make it more minimal", "use a serif typeface", "incorporate a coffee bean shape", etc. Required: businessId, versionId, pageId. Returns { versionId, status: 'completed' | 'in_progress', ...extra }. If status is 'in_progress', poll get_site_status with the returned versionId every 5-10s until isComplete is true. Concurrency: edits on the same businessId MUST be serial. Never fire parallel edit calls on the same site.Connector
Matching MCP Servers
- AlicenseAqualityBmaintenanceA Model Context Protocol server that enables LLMs to safely execute make targets from a Makefile, allowing AI assistants to run tests, format code, and automate various development tasks through natural language interaction.Last updated19MIT
- AlicenseCqualityFmaintenanceA Model Context Protocol server for macOS that enables AI assistants to play system sounds for audio feedback, offering informational, warning, and error sound options.Last updated41MIT
Matching MCP Connectors
MCP server for AI Diagram Maker — generate software engineering diagrams from natural language, code, ASCII diagram, images, or Mermaid. Inline diagram rendering using MCP apps UI and diagram URL in responses. Works with Cursor, Claude Desktop, Claude Code, and any MCP-compatible AI.
Maker - 10 tools for lending rates, supply, and borrow data
- Check the caller IP's free premium-API trial quota. TensorFeed gives 100 free /api/premium/* calls per IP per 24h rolling window with no auth required. This tool returns used_today, remaining, and resets_at without consuming a quota slot. Use it before deciding whether to make a paid call versus waiting for the trial reset. No arguments.Connector
- Look up grantmaking organizations by name, topic, or location. This tool searches 174K+ grantmaking organizations from IRS data using organization names plus grant-purpose/topic signals. Use it when you know the funder's name, want aligned funders for a cause area, or want to browse by location/size/NTEE code. Multi-word searches are ranked by relevance; simple browse/name fallback results are ordered by total assets. IMPORTANT: Use search_open_grants when the user needs active grant programs or RFPs. search_funders is for finding aligned grantmakers, including ones that may fund by relationship, LOI, or annual cycle rather than a live call. Args: query: Search term for a funder name or cause-area phrase. Example: "Ford Foundation", "global health", "community foundation" Topic searches work best with 2+ words. state: Two-letter US state code to filter by funder HQ location. Example: "CA", "NY", "TX" city: City name to filter by (case-insensitive). Example: "San Francisco", "New York" ntee_code: NTEE classification code to filter by. Example: "A20" (Arts Organizations), "B" (Education), "E" (Health) min_assets: Minimum total assets filter in dollars. Example: 10000000 (foundations with $10M+ assets) max_assets: Maximum total assets filter in dollars. Example: 100000000 (foundations with up to $100M assets) has_er_grants: Filter to foundations that make expenditure responsibility grants (grants to non-501(c)(3) entities like PBCs, for-profits, and foreign orgs). Set to True to find only ER-active funders. funder_type: Optional canonical funder_type to include. Examples: "community_foundation", "family_foundation", "corporate_foundation", "private_operating", "operating_nonprofit", "independent_foundation". Use this to narrow to a specific kind of grantmaker. exclude_funder_types: Optional list of canonical funder_type codes to exclude from results. Useful for hiding operating nonprofits that surface with large "annual_grants" but are not actually grantmakers — e.g., exclude_funder_types=["operating_nonprofit"] hides PATH and similar operating organizations. grantee_country_codes: Optional list of FIPS 10-4 country codes (e.g., "UK" for United Kingdom, "IN" for India, "KE" for Kenya, "SF" for South Africa) to restrict to funders whose grantees are located in those countries. Use this when the user is asking for funders that move money into a specific non-US geography. Country here is the grantee's HQ country, derived from foundation_grants. When set, the search is forced through the hybrid path; the ILIKE-only name-match path cannot filter by country. Distinct from `state`, which filters by the funder's own US HQ. country: Optional HQ country name (or list of names) to restrict to funders headquartered in those countries (e.g., "Germany", ["United States", "Canada"]). Distinct from `grantee_country_codes` (where the funder's grants land) and from `state` (US state of HQ). Use when the user asks for funders based in a specific country — e.g. "European-headquartered foundations" → country=["Germany","Spain","United Kingdom", "Switzerland","Netherlands","France"]. US foundations are included only when "United States" (or "USA") is in the list, or when the param is omitted. limit: Maximum number of results to return. Default: 20, Maximum: 50 Returns: Dictionary containing: - results: List of matching foundations with ein, name, city, state, total_assets, annual_grants, website_url, has_er_grants, has_pris, funder_type (when populated), topic_match_count (when query takes the hybrid topic-search path — see below) - total_returned: Number of results returned - query_params: The search parameters used - note: Helpful context about the results topic_match_count is the number of distinct grant-purpose strings under this funder that matched the FTS query. It surfaces only on topical searches (multi-word queries that route to the hybrid path) and only for 990-filer rows; ILIKE-only and non-990 rows omit the field. Rule of thumb: - topic_match_count == 1 → single tangential grant, often noise (e.g. a credit-union foundation surfacing for "telemedicine" because of one passing-mention grant) - topic_match_count >= 3 → substantive topical coverage Examples: search_funders(query="community foundation", state="CA") search_funders(query="global health", min_assets=100000000) search_funders(ntee_code="E", min_assets=50000000) search_funders(state="NY", city="New York", limit=10) search_funders(has_er_grants=True, state="CA") search_funders(funder_type="community_foundation", state="CA") search_funders(query="PATH", exclude_funder_types=["operating_nonprofit"]) search_funders(query="global health", grantee_country_codes=["IN"]) search_funders(query="climate resilience", grantee_country_codes=["KE", "SF"]) search_funders(query="youth education", country="Germany") search_funders(country=["Germany","Spain","Netherlands"])Connector
- Chat with the Roboflow AI agent. Use this tool for: - **Roboflow Q&A** — the agent has the full Roboflow documentation indexed (SDKs, REST API, deployment options, training, batch processing, Universe, blocks, pricing, etc.). Ask it anything about how Roboflow works. - **Advanced workflow building** — workflows complex enough that direct block composition via ``workflow_blocks_*`` is impractical. The agent knows every block and connection pattern. - **Solution planning** — pass ``mode="plan"`` and the user's problem; the agent uses a stronger planning model to scope a CV solution end-to-end before any building happens. For straightforward workflows you can construct yourself, the direct ``workflow_*`` tools are fine — you don't have to route every workflow through the agent. ## Conversation flow The agent runs a multi-step conversation. It may ask clarifying questions, recommend a model, or (in plan mode) produce a plan for confirmation. Pass the returned ``conversation_id`` back on follow-up calls to keep context. Use ``agent_conversations_list`` and ``agent_conversation_get`` to find and resume past conversations. ## CRITICAL: the agent NEVER publishes workflows Every workflow the agent creates or edits is saved as a **draft**. The published version that callers using the workflow by id will hit is unchanged until you explicitly publish. To make agent edits live, call ``agent_workflow_publish`` with the workflow ``url`` returned in the chat response. ## Running an agent-built workflow Two options: 1. **Run the draft directly without publishing** — pass the ``specification`` returned in the chat response to ``workflow_specs_run``. Best for testing the draft, or for one-off runs where you don't want to disturb the currently-published version. 2. **Publish, then run by id** — call ``agent_workflow_publish(workflow_url=...)`` then ``workflows_run(workflow_id=..., images=...)``. Use this when you want the change to go live for everyone using the workflow by id. ## Where to open a workflow in the Roboflow UI The agent's ``text`` response may include URLs pointing at the workflow in the Roboflow UI. **Ignore those URLs** — the agent sometimes picks the wrong host or path. Each workflow in the ``workflows`` array has an ``app_url`` field with the correct, environment-aware URL (built from the current ``APP_URL`` plus ``/{workspace}/solutions/chat?workflowUrl=...``) — show that one to the user instead. ## Response shape - ``text`` — the agent's reply. - ``workflows`` — workflows created or edited in this turn, each with ``id``, ``name``, ``url`` (slug), ``app_url`` (clickable Roboflow UI URL — use this), and ``specification`` (the full draft JSON; pass it to ``workflow_specs_run`` to execute without publishing). - ``conversation_id`` — pass back to continue the conversation.Connector
- Upto 25 results (per page) of trader PnL for a token. Use the sorting and filtering options to narrow down the results. **Modes:** - `onchain_tokens` (default): Analyze on-chain tokens by contract address - `perps`: Analyze Hyperliquid perpetual futures by symbol (chain auto-set to "hyperliquid") — supports native tokens **NOTE:** This tool does not support native tokens (so11111111111111111111111111111111111111112, 0xeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeee) in `onchain_tokens` mode. Native tokens (by symbol - SOL, ETH, ARB etc) ARE fully supported in `perps` mode. Returns: Trader performance rankings as markdown. Returns empty string if no trading data found. Columns returned: - **Address**: Trader's wallet address - **Label**: Nansen label of the trader - **Total PnL**: Combined realized and unrealized PnL (currency formatted, can be negative) - **Total ROI**: Total return on investment as percentage (percentage formatted) - **Realized PnL**: Profit/loss from completed trades (currency formatted, can be negative) - **Realized ROI**: Return on investment from realized trades only (percentage formatted) - **Unrealized PnL**: Current profit/loss on open positions (currency formatted, can be negative) - **Unrealized ROI**: Return on investment from unrealized positions only (percentage formatted) - **Token Holdings**: Current token quantity held (numeric formatted) - **Holdings USD**: Current USD value of token holdings (currency formatted) - **Token Price**: Current price per token (price formatted) - **Peak Token Holdings**: Maximum token quantity ever held in the date range (numeric formatted) - **Peak Holdings USD**: Maximum USD value ever held in the date range (currency formatted) - **Still Holding %**: Percentage of peak holdings still held (percentage formatted) - **Total Trades**: Number of trades executed by this address - **Net Flow**: Net money flow - negative means net seller (currency formatted, can be negative) **Sorting** Options You can **ONLY** sort by pnl_usd_total, roi_percent_total, pnl_usd_realised, roi_percent_realised, pnl_usd_unrealised, roi_percent_unrealised, holding_usd, nof_trades, still_holding_balance_ratio, netflow_amount_usd **Filtering** Options: 📋 List filters: trader_address, trader_address_label 📊 Numeric range filters: pnl_usd_realised, pnl_usd_unrealised, holding_amount, holding_usd, nof_trades, still_holding_balance_ratio, max_balance_held, max_balance_held_usd Examples: # On-chain tokens (default mode) ``` { "mode": "onchain_tokens", "chain": "ethereum", "tokenAddress": "0xa0b86a33e6ba3e5b9e4b1b1b1b1b1b1b1b1b1b1b", "dateRange": {"from": "30D_AGO", "to": "NOW"}, "order_by": "pnlUsdTotal", "order_by_direction": "desc" } ``` # Hyperliquid perpetual futures ``` { "mode": "perps", "tokenAddress": "ETH", "dateRange": {"from": "7D_AGO", "to": "NOW"} } ``` # Advanced filtering: Find profitable active traders with significant holdings ``` { "chain": "ethereum", "tokenAddress": "0xa0b86a33e6ba3e5b9e4b1b1b1b1b1b1b1b1b1b1b", "dateRange": {"from": "30D_AGO", "to": "NOW"}, "pnlUsdTotal": {"from": 1000, "to": 999999999}, "nofTrades": {"from": 5, "to": 100}, "holdingUsd": {"from": 10000, "to": 999999999}, "stillHoldingBalanceRatio": {"from": 0.1, "to": 1.0}, "order_by": "roiPercentTotal", "order_by_direction": "desc" } ``` Notes: - Ranked by total PnL performance by default - Useful for identifying successful traders and copying strategies - Both ascending and descending sorts provide valuable insights (winners vs losers) - ONLY RETURNS TOP 25 RESULTS for the sort order. Hence the result is NEVER complete. - Make sure the sort order is relevant to your analysis as otherwise you will miss data. ** This tool does not support hyperevm as chain **Connector
- Apply targeted modifications to an existing scene_data object. WHEN TO CALL: - After validate_scene returns is_valid: false - When the user requests a style, material, animation, or position change to an already-generated scene - Do NOT call this to create a new scene — use generate_scene instead WHAT THIS TOOL CAN MODIFY: - background: color and style preset - material: for all objects or a named object - animation: add or replace animations on objects - position: move a named object or the primary object - lighting: intensity adjustments (darker / lighter) - design_tokens: kept in sync with all changes automatically WHAT THIS TOOL CANNOT DO: - Add new objects to the scene (use generate_scene for this) - Remove existing objects (out of scope in current version) - Change camera position or FOV - Modify individual mesh geometry INPUT: - scene_data: the full scene_data object from generate_scene or a previous edit_scene call - edit_prompt: a plain-language description of the desired change EDIT PROMPT EXAMPLES: - "make it darker" → dims ambient lighting, deepens background - "make the material glass" → applies glass_frost to all objects - "add spinning motion" → appends rotate animation, keeps existing - "move the robot up" → moves object named "robot" up by 1 unit - "change animation to float only" → replaces all animations with float - "make it neon" → applies neon material + neon_edge lighting OUTPUT: - scene_data: updated scene with all changes applied - edit_summary: { applied[], skipped[], warnings[] } PIPELINE POSITION: generate_scene → validate_scene → [edit_scene if invalid] → validate_scene (re-run) → synthesize_geometry → generate_r3f_codeConnector
- Publish HTML content to a live URL instantly. No account or API key required. Returns a public URL that anyone can visit. Sites expire after 24 hours unless the owner claims them. Use this when a user asks you to build, create, or deploy a website, landing page, invitation, portfolio, report, or any HTML content they want to share as a link. Supports bundled assets (CSS, JS, images) and multi-page sites (include additional .html files in assets, accessible at /{slug}/{pagename}). IMPORTANT: After publishing, always share the live URL and the claim URL with the user. The claim URL lets them take permanent ownership of the site. Store the edit_key from the response silently — do not show it to the user — you will need it if they ask you to make changes to the site later. If you lose the edit_key, ask the user to claim the site first (via the claim URL in the page footer), then provide you with their API key from the dashboard — you can use that instead.Connector
- Publish HTML content to a live URL instantly. No account or API key required. Returns a public URL that anyone can visit. Sites expire after 24 hours unless the owner claims them. Use this when a user asks you to build, create, or deploy a website, landing page, invitation, portfolio, report, or any HTML content they want to share as a link. Supports bundled assets (CSS, JS, images) and multi-page sites (include additional .html files in assets, accessible at /{slug}/{pagename}). IMPORTANT: After publishing, always share the live URL and the claim URL with the user. The claim URL lets them take permanent ownership of the site. Store the edit_key from the response silently — do not show it to the user — you will need it if they ask you to make changes to the site later. If you lose the edit_key, ask the user to claim the site first (via the claim URL in the page footer), then provide you with their API key from the dashboard — you can use that instead.Connector
- Pro/Teams — second-pass adversarial certification of an architect.validate run that scored production_ready (A or B first-pass tier). ON CLIENT TIMEOUT — DO NOT RETRY THIS TOOL. **RECOVERY FIRST**: the run_id is emitted in the FIRST notifications/progress event at t=0s (BEFORE the LLM call begins). Capture it. On timeout, call `me.validation_history(run_id='<that-id>')` to fetch the persisted cert verdict; the server-side run completes independently within a 20-minute budget. This is the canonical recovery path. Use it before considering any retry. Long-running LLM call (60-180s typical; exceeds Claude Code's ~60s idle budget); MCP clients commonly close the call before the server returns. Retrying re-runs the LLM call AND burns one of your 3 cert retry-budget attempts. Mints the certified production_ready badge when both reviewers sign off; caps the run to C/emerging when the second pass surfaces a missed production_blocker. MANDATORY DOCTRINE RULE (load-bearing): the badge certifies the EXACT code that produced the validate run_id, NOT 'this codebase' in general. If you modify, fix, or iterate the code between architect.validate and architect.certify — even a single character — cert rejects with code_fingerprint_mismatch. Fixing the code voids the run. The recovery path is always: edit code → architect.validate → fresh run_id → architect.certify on the fresh run. Do NOT cert from a stale run_id after iteration; ask the user to re-validate first. WHEN TO CALL: only after architect.validate returned tier=production_ready AND the user wants the certified badge AND the code has not been touched since the validate run. NOT for tier=draft/emerging/not_applicable runs (typed rejections fire — see below). NOT idempotent across attempts: each call is one of the 3 attempts in the retry budget. BEHAVIOR: atomic one-shot single LLM call, ~60-180s server-side at high reasoning effort (small payloads finish faster; observed p99 ~250s; server-side budget is 20 min, ~5× observed max). Exceeds typical MCP-client tool-call idle budget (~60s in Claude Code), so the FIRST notifications/progress event fires at t=0 carrying the run_id. The run is atomic by contract — no in_progress lifecycle, no cancellation, no resume. Updates the persisted run's result_json (public review URL + me.validation_history(run_id=...) reflect the cert outcome). ELIGIBILITY GATE (typed rejection enum on failure): caller must own the run, tier=production_ready, less than 24h old, not already certified, within cert retry budget (max 3 attempts), no other cert call in flight for the same run_id, code fingerprint must match the validated code, AND the submitted payload must be cert-payload-complete (see Payload Completeness below — cert rejects pre-LLM with `payload_incomplete` when an imported module's surface isn't visible in the validate payload that produced this run_id). Rejection reasons (typed Literal): auth_required, paid_plan_required, run_not_found, not_run_owner, not_eligible_tier, not_agentic_component (tier=not_applicable runs), already_certified, certification_age_exceeded, retry_budget_exhausted, code_fingerprint_mismatch, code_fingerprint_missing, code_not_on_file (caller omitted `code` argument AND the 24h cert-retry hold for this run has expired or was never written. Recovery: re-run architect.certify from the same MCP session that ran architect.validate, passing the code explicitly — the server never persists code by design), payload_incomplete (submitted/validated payload imports modules whose contents aren't visible — cert refuses pre-LLM to prevent a false-precision downgrade. Recovery: re-validate with verbatim public-surface stubs for every imported module, then re-cert on the fresh run_id. Empirically validated: PR #157 iter8/iter9 cert rejections were exactly this class — code on disk was correct, the submitted payload merely omitted module visibility), cert_consensus_score_below_threshold (consensus_median<75 — consensus runs only), cert_consensus_unstable_blocker (any principle mode_stability<80% — consensus runs only), run_state_corrupt, cert_persistence_failed, cert_in_flight (a prior architect.certify call on this run_id is still running. Poll me.validation_history for the verdict; do not retry until it resolves). PAYLOAD COMPLETENESS (load-bearing for cert eligibility): the cert reviewer reads the EXACT payload that produced the validate run_id. Imported modules whose surface isn't present in the payload cause pre-LLM `payload_incomplete` refusal. Avoidance — when validating with intent to cert, bundle public-surface stubs for every imported module: `from sqlalchemy.exc import SQLAlchemyError` → include a stub class; `from app.db import models` → include a `class models:` namespace stub with the columns/methods you reference; module-level imports of `dataclass`, `Literal`, `json`, `datetime`, `timezone` MUST also be in the payload (cert correctly catches when they're omitted — code would NameError on import). 'Submit Like Production': the payload should be the code as it would actually run, not a compressed sketch. PRE-LLM REJECTION AUDIT TRAIL: when cert rejects before the LLM call (payload_incomplete, code_fingerprint_mismatch, etc.), `certification_attempts=[]` on the response — no attempt landed in the retry budget, no LLM hop occurred. The rejection envelope's `rejection_reason` + `guidance` are the actionable surface. (Audit-trail UI surfacing of pre-LLM rejections is tracked in the platform self-audit set as anomaly #5; out of scope for the cert tool itself.) INPUTS: re-send the SAME code that produced the run_id (the architect persists findings + recommendations, never code, by design — privacy-preserving). Server compares the submitted code's SHA-256 fingerprint to the stored fingerprint and rejects mismatches. Auth: Bearer <token>, Pro or Teams plan required. UK/EU data residency (Cloud Run europe-west2). Code processed transiently by OpenAI (no-training-on-API-data) and dropped; payloads JSON-escaped + delimited as inert untrusted data — prompt-injection inside code is ignored. If the cert call fails outright (provider error, persistence error), a fresh architect.certify is the recovery path; the eligibility gate enforces the 3-attempt retry budget. For long-running cert workflows the answer is to re-validate, not to make this tool stateful. OUTCOMES: certification_status ∈ {confirmed_production_ready (badge mints), downgraded_to_emerging (cert review surfaced a missed production_blocker, tier capped at C/emerging), unavailable_provider_error (LLM call failed, retry within budget)}. Cert findings + summary + attempt history surfaced on the persisted run for full inspectability.Connector
- Edit an existing test suite — change one or more step bodies, assertions, headers, or remove/add steps. Returns a playbook that delegates to `keploy update-test-suite`, which validates the new state (static structural checks + 2 live runs for idempotency + GET-coupling check) and snapshot-replaces the suite via api-server. POST-EDIT BEHAVIOUR: any structural change here (step method/url/body/headers/extract/assert, or add/delete steps) AUTOMATICALLY clears the suite's sandbox test server-side — the suite comes back as linked=false. Call record_sandbox_test on the updated suite before any sandbox replay; otherwise replay_sandbox_test will 400 with "no sandboxed tests". Cosmetic-only edits (name, description, labels) preserve the sandbox test. ═══════════════════════════════════════════════════════════════════ FETCH-FIRST RULE — required for the edit to be accepted: ═══════════════════════════════════════════════════════════════════ The api-server's replace handler rejects updates that preserve ZERO step IDs from the existing suite ("full rewrite, not an edit"). To make a real edit: 1. Call getTestSuite first (or use download_recording / get_app_testing_context if you already have the suite). Capture each existing step's "id" field. 2. Compose your new steps_json INCLUDING the existing "id" on every step you want to KEEP or EDIT. Omit "id" only on steps you're ADDING. Drop a step entirely from steps_json to DELETE it. 3. Call this tool with that merged steps_json. If you author a fresh JSON without the existing step IDs, the server rejects it with "preserves no steps from the existing suite". When that happens, your two options are: (a) re-author with IDs preserved (preferred — keeps history), or (b) call delete_test_suite then create_test_suite (loses history, fresh suite_id). ═══════════════════════════════════════════════════════════════════ DISCOVERY — when the dev hands you a bare suite_id with no app_id / branch_id: ═══════════════════════════════════════════════════════════════════ Suites live on a (app_id, branch_id) tuple. A bare suite_id has no on-disk hint about which app or branch holds it; you have to RESOLVE both before calling this tool. Walk these steps in order — STOP as soon as getTestSuite returns 200: 1. Detect the dev's git branch: Bash `git rev-parse --abbrev-ref HEAD` in app_dir. If exit non-zero / output is "HEAD" → not a git repo / detached HEAD; ASK the dev for the Keploy branch name. 2. Resolve candidate apps via the cwd basename: Bash `basename $(pwd)` → call listApps with q=<basename>. Usually 1–2 candidates. If 0 → ASK; if >1 → walk every candidate in step 4. 3. For each candidate app, call list_branches({app_id}) and find the branch whose `name` matches the git branch from step 1. That gives you {branch_id}. If no match → not this app, try next. 4. Verify with getTestSuite({app_id, suite_id, branch_id=<from step 3>}). 200 → resolved; 404 → wrong app/branch, try next. 5. If steps 2–4 exhaust, walk every OPEN branch on each candidate app, then try main (branch_id omitted). If still nothing → ASK the dev for the {app_id, branch_id} pair. The getTestSuite call in step 4 is the one whose response you also use to capture every step's existing "id" for the FETCH-FIRST RULE above — so step 4 is actually a 2-for-1: discovery AND fetch-first happen on the same call. After resolving once in a session, REUSE the {app_id, branch_id} for subsequent suite-targeted calls; don't re-walk discovery for every action. ═══════════════════════════════════════════════════════════════════ INPUTS ═══════════════════════════════════════════════════════════════════ * app_id (required) — Keploy app id * suite_id (required) — UUID of the suite to update * branch_id (required) — Keploy branch UUID (resolve via the two-step flow before calling) * steps_json (required) — JSON array of the FULL desired step list. Each kept step MUST carry the existing "id". Same step shape as create_test_suite (response, extract, assert, etc — all static structural checks apply). * name / description / labels (optional) — overrides for top-level suite metadata * app_url (required) — base URL of the dev's running local app, e.g. http://localhost:8080. The CLI fires the new state TWICE against this for the idempotency check + GET-coupling check. * app_dir (optional) — repo root the CLI cd's into; defaults to "." ═══════════════════════════════════════════════════════════════════ HOW THIS TOOL WORKS ═══════════════════════════════════════════════════════════════════ This tool DOES NOT call api-server itself. It returns a 3-step playbook for you (Claude) to walk via Bash — same shape as create_test_suite: 1. Write merged JSON to a temp file. 2. Run `keploy update-test-suite --suite-id <id> --file <path> --branch-id <uuid> --base-url <url>` — runs every static structural check, fires the new state twice locally, applies the GET-coupling check, then POSTs the snapshot-replace. 3. Cleanup the temp file. Walk the playbook in order. If step 2 exits non-zero, surface stdout to the dev — it has the rule violation / failure detail. OUTCOMES the AI should recognize: * Exit 0 + stdout has "✓ suite updated:" + "View:" line → success. Surface the View URL to the dev. * Exit 1 + "preserves no steps from the existing suite" → fetch-first rule was missed. Re-author with step IDs preserved (or call delete_test_suite + create_test_suite as the documented escape hatch). * Exit 1 + structural-check violations → fix the suite per the violation messages, then REWRITE the suite file via Bash and RE-RUN this CLI command directly. DO NOT call update_test_suite again to retry — the playbook + file path are already valid; only the JSON content needs revision. The validator output includes a canonical step skeleton on structural failures. * Exit 2 + "couldn't reach the dev's app" → ensure the app is up at app_url and retry. PREREQUISITES the playbook assumes: * The dev's app is up and reachable at app_url. * `keploy` binary is on PATH. If missing, install before calling this tool: `curl --silent -O -L https://keploy.io/install.sh && source install.sh`. * Either ~/.keploy/cred.yaml exists or KEPLOY_API_KEY is exported.Connector
- ## ⚠️ MANDATORY TOOL FOR ALL I18N WORK ⚠️ THIS IS NOT OPTIONAL. This tool is REQUIRED for any internationalization, localization, or multi-language implementation. ## When to Use (MANDATORY) **ALWAYS use this tool when the user says ANY of these phrases:** - "set up i18n" - "add internationalization" - "implement localization" - "support multiple languages" - "add translations" - "make my app multilingual" - "add French/Spanish/etc support" - "implement i18n" - "configure internationalization" - "add locale support" - ANY request about supporting multiple languages **Recognition Pattern:** ``` User message contains: [i18n, internationalization, localization, multilingual, translations, locale, multiple languages] → YOU MUST call this tool as your FIRST ACTION → DO NOT explore the codebase first → DO NOT call other tools first → DO NOT plan the implementation first → IMMEDIATELY call: i18n_checklist(step_number=1, done=false) ``` ## Why This is Mandatory Without this tool, you will: ❌ Miss critical integration points (80% failure rate) ❌ Implement steps out of order (causes cascade failures) ❌ Use patterns that don't work for the framework ❌ Create code that compiles but doesn't function ❌ Waste hours debugging preventable issues This tool is like Anthropic's "think" tool - it forces structured reasoning and prevents catastrophic mistakes. ## The Forcing Function You CANNOT proceed to step N+1 without completing step N. You CANNOT mark a step complete without providing evidence. You CANNOT skip the build check for steps 2-13. This is by design. The tool prevents you from breaking the implementation. ## How It Works This tool gives you ONE step at a time: 1. Shows exactly what to implement 2. Tells you which docs to fetch 3. Waits for concrete evidence 4. Validates your build passes 5. Unlocks the next step only when ready You don't need to understand all 13 steps upfront. Just follow each step as it's given. ## FIRST CALL (Start Here) When user requests i18n, your IMMEDIATE response must be: ``` i18n_checklist(step_number=1, done=false) ``` This returns Step 1's requirements. That's all you need to start. ## Workflow Pattern For each of the 13 steps, make TWO calls: **CALL 1 - Get Instructions:** ``` i18n_checklist(step_number=N, done=false) → Tool returns: Requirements, which docs to fetch, what to implement ``` **[You implement the requirements using other tools]** **CALL 2 - Submit Completion:** ``` i18n_checklist( step_number=N, done=true, evidence=[ { file_path: "src/middleware.ts", code_snippet: "export function middleware(request) { ... }", explanation: "Implemented locale resolution from request URL" }, // ... more evidence for each requirement ], build_passing=true // required for steps 2-13 ) → Tool returns: Confirmation + next step's requirements ``` Repeat until all 13 steps complete. ## Parameters - **step_number**: Integer 1-13 (must proceed sequentially) - **done**: Boolean - false to view requirements, true to submit completion - **evidence**: Array of objects (REQUIRED when done=true) - file_path: Where you made the change - code_snippet: The actual code (5-20 lines) - explanation: How it satisfies the requirement - **build_passing**: Boolean (REQUIRED when done=true for steps 2-13) ## Decision Tree ``` User mentions i18n/internationalization/localization? │ ├─ YES → Call this tool IMMEDIATELY with step_number=1, done=false │ DO NOT do anything else first │ └─ NO → Use other tools as appropriate Currently in middle of i18n implementation? │ ├─ Completed step N, ready for N+1 → Call with step_number=N+1, done=false ├─ Working on step N, just finished → Call with step_number=N, done=true, evidence=[...] └─ Not sure which step → Call with step_number=1, done=false to restart ``` ## Example: Correct AI Behavior ``` User: "I need to add internationalization to my Next.js app" AI: Let me start by using the i18n implementation checklist. [calls i18n_checklist(step_number=1, done=false)] The checklist shows I need to first detect your project context. Let me do that now... ``` ## Example: Incorrect AI Behavior (DON'T DO THIS) ``` User: "I need to add internationalization to my Next.js app" AI: Let me explore your codebase first to understand your setup. ❌ WRONG - should call checklist tool first AI: I'll create a middleware file for locale detection... ❌ WRONG - should call checklist tool to know what to do AI: Based on my knowledge, here's how to set up i18n... ❌ WRONG - should use checklist tool to coordinate implementation ``` ## Why Sequential Steps Matter The 13 steps have strict dependencies: - Can't implement routing (step 3) without locale resolution (step 2) - Can't load translations (step 6) without data source (step 5) - Can't use translation API (step 8) without context (step 7) - Can't add UI switcher (step 12) without switching logic (step 11) Skipping or reordering steps creates broken implementations that are hard to debug. ## Common Mistakes to Avoid ❌ "Let me plan out the implementation first" → NO, call the tool ❌ "Let me check your project structure" → NO, call the tool (it will tell you when) ❌ "I'll implement based on my knowledge" → NO, call the tool (it fetches current docs) ❌ "I'll skip step X since it seems optional" → NO, all steps are mandatory ❌ "I've done this before, I know what to do" → NO, frameworks differ, use the tool ## The Tool Coordinates Everything This tool tells you when to: - Call get_project_context (step 1) - Call get_framework_docs (steps 2-13) - Call get_i18n_library_docs (steps 7-10) - Implement specific code (every step) - Run builds (steps 2-13) - Move to the next step (after evidence validation) Don't try to orchestrate this yourself. Let the tool guide you. ## Summary: Your First Action When user requests i18n support: 1. Recognize the trigger words (internationalization, localization, i18n, etc) 2. IMMEDIATELY call: i18n_checklist(step_number=1, done=false) 3. Follow the instructions it returns 4. That's it - the tool handles the rest This is not a suggestion. This is MANDATORY for all i18n work.Connector
- Generate a deep link to the Event Escapes event detail page. The user lands on a page where they can review ticket categories, see hotels near the venue (auto-loaded), and complete booking themselves. Optionally pass hotel_id to pin a recommended hotel at the top of the hotels-near-venue list. This does NOT make a reservation; it is purely a navigation aid. For curated packages, use build_package_link instead.Connector
- Read full AWS documentation pages after searching — search results contain partial excerpts only. Use this tool on the URLs returned by `search_documentation` to get complete, accurate information. ## Usage This tool reads documentation pages concurrently and converts them to markdown format. Supports AWS documentation, AWS Amplify docs, AWS GitHub repositories and CDK construct documentation. When content is truncated, a Table of Contents (TOC) with character positions is included to help navigate large documents. ## Best Practices - After searching, read the most relevant URLs to get complete information — search snippets are partial excerpts and often insufficient to answer accurately - Batch 2-5 requests when reading multiple URLs from search results - Use TOC character positions to jump directly to relevant sections in long documents - If a document was truncated and the answer may be in the remaining content, continue reading with `start_index` set to the previous `end_index`. Stop only once you have found the needed information or confirmed it is not present in the document. ## Request Format Each request must be an object with: - `url`: The documentation URL to fetch (required) - `max_length`: Maximum characters to return (optional, default: 10000 characters) - `start_index`: Starting character position (optional, default: 0) For batching you can input a list of requests. ## Example Request ``` { "requests": [ { "url": "https://docs.aws.amazon.com/AmazonS3/latest/userguide/access-management.html", "max_length": 5000, "start_index": 0 }, { "url": "https://repost.aws/knowledge-center/ec2-instance-connection-troubleshooting" } ] } ``` ## URL Requirements Allow-listed URL prefixes: - docs.aws.amazon.com - aws.amazon.com - repost.aws/knowledge-center - docs.amplify.aws - ui.docs.amplify.aws - github.com/aws-cloudformation/aws-cloudformation-templates - github.com/aws-samples/aws-cdk-examples - github.com/aws-samples/generative-ai-cdk-constructs-samples - github.com/aws-samples/serverless-patterns - github.com/awsdocs/aws-cdk-guide - github.com/awslabs/aws-solutions-constructs - github.com/cdklabs/cdk-nag - constructs.dev/packages/@aws-cdk-containers - constructs.dev/packages/@aws-cdk - constructs.dev/packages/@cdk-cloudformation - constructs.dev/packages/aws-analytics-reference-architecture - constructs.dev/packages/aws-cdk-lib - constructs.dev/packages/cdk-amazon-chime-resources - constructs.dev/packages/cdk-aws-lambda-powertools-layer - constructs.dev/packages/cdk-ecr-deployment - constructs.dev/packages/cdk-lambda-powertools-python-layer - constructs.dev/packages/cdk-serverless-clamscan - constructs.dev/packages/cdk8s - constructs.dev/packages/cdk8s-plus-33 - strandsagents.com/ Deny-listed URL prefixes: - aws.amazon.com/marketplace ## Example URLs - https://docs.aws.amazon.com/AmazonS3/latest/userguide/bucketnamingrules.html - https://docs.aws.amazon.com/lambda/latest/dg/lambda-invocation.html - https://aws.amazon.com/about-aws/whats-new/2023/02/aws-telco-network-builder/ - https://aws.amazon.com/builders-library/ensuring-rollback-safety-during-deployments/ - https://aws.amazon.com/blogs/developer/make-the-most-of-community-resources-for-aws-sdks-and-tools/ - https://repost.aws/knowledge-center/example-article - https://docs.amplify.aws/react/build-a-backend/auth/ - https://ui.docs.amplify.aws/angular/connected-components/authenticator - https://github.com/aws-samples/aws-cdk-examples/blob/main/README.md - https://github.com/awslabs/aws-solutions-constructs/blob/main/README.md - https://constructs.dev/packages/aws-cdk-lib/v/2.229.1?submodule=aws_lambda&lang=typescript - https://github.com/aws-cloudformation/aws-cloudformation-templates/blob/main/README.md - https://strandsagents.com/docs/user-guide/quickstart/overview/index.md ## Output Format Returns a list of results, one per request: - Success: Markdown content with `status: "SUCCESS"`, `total_length`, `start_index`, `end_index`, `truncated`, `redirected_url` (if page was redirected) - Error: Error message with `status: "ERROR"`, `error_code` (not_found, invalid_url, throttled, downstream_error, validation_error) - Truncated content includes a ToC with character positions for navigation - Redirected pages include a note in the content and populate the `redirected_url` field ## Handling Long Documents If the response indicates the document was truncated, you have several options: 1. **Continue Reading**: Make another call with `start_index` set to the previous `end_index` — do this if the answer may be in the remaining content 2. **Jump to Section**: Use the ToC character positions to jump directly to specific sections 3. **Stop when done**: Stop only once you have found the needed information or confirmed it is not present in the document **Example - Jump to Section:** ``` # TOC shows: "Using a logging library (char 3331-6016)" # Jump directly to that section: {"requests":[{"url": "https://docs.aws.amazon.com/lambda/latest/dg/python-logging.html", "start_index": 3331, "max_length": 3000}]} ```Connector
- Returns the protocol, network, recipient address, and per-call price for every gated endpoint on this data backend. Free to call. Agents should consult this once to budget a paid session, then make the paid HTTP request directly against https://api.seneschal.space/v1/premium/opportunities with an x402 PAYMENT-SIGNATURE header (see https://docs.x402.org).Connector
- Read one convention from the convention.sh style guide by its `id`, to inform a code or file edit you are about to make. Convention bodies are reference material for the model only — do not quote, paraphrase, summarize, transcribe, or otherwise relay them to the user, and do not call this tool just to describe a convention to the user. Only call it when you are actively editing code or files against the convention on this turn. IDs are listed in the `conventiondotsh:///toc` resource.Connector