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650,527 tools. Updated 2026-10-14 04:26

"A tool or library for executing Python code" matching MCP tools:

  • Check Python source without running it: parse, lint (ruff), type-check (mypy), AST security policy, credential scan. Safe on code you do not trust. Use it on every Python file you generated or edited, before writing it to disk. Alternatives: repair_python to get the corrected source instead of the diagnosis; execute_python to prove the code runs. Auth: a key is required. A free key covers this call, 25 per day, then HTTP 429; get one with POST /v1/keys. Credits are bought without an account, 1 per call: GET /v1/pricing says where to send the xDAI. Or pay for this one call with no key at all: call it without one and the result carries x402 payment requirements ($0.01 in USD Coin on eip155:8453); sign them and repeat the call with the payment in _meta['x402/payment']. Arguments: code: the whole file, 1..200000 bytes of UTF-8 measured after encoding (empty is refused with 400, larger with 413); a fragment is fine, but line and column numbers in the answer count from 1 in what you sent. language: must be 'python'; anything else is 400, and the field may be omitted. Of options only transpile_to (e.g. 'javascript', which returns a translated copy in transpiled) acts here; timeout_s, max_iterations, optimize, examples and expected_output need a pass that rewrites or runs the code, so send code alone. Ignored options are not refused, so a call that sets them looks like it worked; and code that does not parse is answered rather than refused: valid=false with the syntax error located, which is the point. Returns valid, score 0..1, diagnostics (rule, message, line, column), security findings, fixes, fixed_code and runtime; see outputSchema. The code and its verdict are retained to improve the service.
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  • Get the actual Python code behind a community leaderboard strategy. Use after `browse_community`: pass an entry's `id` here to read its real `feature_engineering()` + `strategy_config()` source so the user can inspect or tweak it. To deploy it unchanged, pass the same id to `one_shot` as `community_id`. Read-only, no signup needed. Args: community_id: The `id` of a community entry (from `browse_community`). Returns: dict with: id, title, username, description, symbol, timeframe, metrics {total_ret, win_rate, profit_factor, n_trades, mdd, sharpe_strat}, and `code` (the full Python source). SHOW the code to the user, and offer to deploy it via one_shot(community_id=...) or tweak it first.
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  • FREE — quote a paid tool's cost WITHOUT executing it or charging the wallet (call this before assess_property_hazard etc.). `units` = candidate/item count for per-unit-priced tools (currently only rank_hazard_safe_sites — pass the number of candidates you intend to submit); ignored (treated as 1) for every flat-priced tool. Returns JSON: {ok, tool_name, units, credits, eur_estimate} for credits-wallet tools; for assess_property_hazard_x402 (paid on-chain via x402, not the credits wallet) returns {ok, tool_name, units, payment, price_usd} instead; or {ok: false, error} for an unknown/free tool name.
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  • Use when the user asks to inspect, summarize, or visualize an attached TFLite, ONNX, GGUF, SafeTensors, Core ML, or ExecuTorch deployment artifact. Opens a browser-local analyzer, returns a hash-bound format-neutral Model IR table and static evidence summary, and provides deterministic document views without executing model code. Do not use for training checkpoints, measured latency, task accuracy, clinical validity, regulatory compliance, or actual runtime placement.
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  • Run a code snippet inside a sandbox and return {exit_code, stdout, stderr, timed_out, truncated, duration_ms}. `language`: python (Python 3.12), node (Node.js 22) or bash. Install packages first with exec_command (`pip install …` / `npm i …`). The code is passed to the interpreter on stdin, so it cannot read stdin itself. `timeout` seconds (default 30, max 55 per call). LONGER JOBS: set `background: true` — returns a `task_id` immediately (timeout then defaults to the sandbox's remaining lifetime); poll get_task with the returned offsets to read new output and the exit code, kill_task to stop it. The sandbox keeps billing while a task runs and is not idle-deleted. Output is capped at 1 MiB per stream for synchronous calls. Files and installed state persist between calls in the same sandbox. Requires: token + live sandbox_id. Typical: timed_out after 55 s → repeat with background: true.
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  • Return a self-contained stdlib Python client for scoring at ZERO per-call LLM tokens. Purpose: Hand the caller an HTTP consumer that runs locally so bulk scoring doesn't burn LLM tokens per book. Use when: You need to score more than ~200 books, or `kirk_score_book_batch` returned `batch_too_large`, or the caller is running an autonomous bulk workload that would otherwise pay per-tool-call LLM tokens for every book. Do not use when: You are running a one-off interactive call — a direct `kirk_score_book` invocation is simpler; don't route through the client for a single book. Capability class(es): Cost-steering / delivery-path tool. Hands the caller a runner that exercises the same C2 / C5 / C6 capabilities as the MCP scoring tools, but at zero per-call LLM token cost. Path fit: The returned client is an HTTP consumer of the same MCP endpoint. Production integrations run in-process under sealed-engine attestation — same binary sha as this endpoint. Contact Kavara for deployment options. Cost: 0 IU. Free tool. Once running locally, the returned client bills against the same tools it drives: single-book calls at 1 IU each, and batch calls at 1 IU per 50 books (minimum 1 IU per call). A full 500-book batch → 10 IU. No LLM tokens on top. Cost comparison (2.7M-book validation rerun via 500-book batches — ~5400 batches, 54000 IU billed either way): MCP via Sonnet 5: $1,968 LLM + $540 IU + ~15 days wall clock MCP via Haiku 4.5: $656 LLM + $540 IU + ~10 days Python client (this tool): $0 LLM + $540 IU + ~55 min Return structure: { "language": "python", "filename": "kirk_online_client.py", "requirements": str, "usage": str, "code": str (the client source, ~500 LOC), "example": str (2-line copy-paste demo) }
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  • Proves AI-generated Python does what you asked: lint, types, security, sandbox run, exact fixes.

  • Security + bug + perf + refactor audit for Python. Returns 0-10 score + MD report.

  • Run a sandbox backtest of strategy code without persisting anything. This is the fastest way to test a strategy. The code is run through static checks and a full backtest on historical data, but no Strategy or StrategyVersion rows are created. Use this for rapid iteration. Args: code: Python source code implementing the Strategy contract. Must define a METADATA dict and a class extending Strategy with an on_bar(ctx) -> Signal method. See CREATOR_API.md. domain: Trading domain (e.g. "eth_usdc", "btc_usdc", "sol_usdc"). symbol: Price symbol for historical data (e.g. "ETHUSDT"). user_id: Identifier for trial tracking (used for DSR correction). Returns JSON with: success, metrics (sharpe, sortino, win_rate, total_trades, return_bps, max_drawdown, regime_breakdown, exit_reason_breakdown), or error details if validation failed.
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  • Run the named DecisionGATE five sequential gates on a proposal (Freedom without clarity is chaos) without executing a catalog product. Also runs automatically inside fraggate_call before exec. Use this when you want a gate check without executing a catalog product verb. Do not use it for executing a product op or searching the library; use fraggate_call or library_lookup instead. Write: appends an ask/refuse ledger tip (not idempotent). Empty {} still runs the five gates and stamps the ledger. Does not execute domain software. Named wrapper — same DecisionGATE kernel; not the full MASTER-33 hop list; Softwares exec stays fraggate_call. All proposal fields are optional. Missing evidence can fail a gate. accountable identity on this runtime is Aziel Eliab only. Mutation requires confirm=true (runtime gate) or dry_run=true (preview only, no write). confirm and dry_run stay optional on inputSchema.required. dry_run still returns FG-HALLUC-TOOL, FG-STUB, FG-UNKNOWN-OP, or FG-LOCAL-ONLY when a confirm call would refuse. confirm is consent, not tenant auth, and it does not upgrade shared public-demo isolation. Returns gate view, final_state, ledger_tip, and result (code FG-OK on the named module wrapper).
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  • Open one hub product card by slug (name, version, skill, download, ops) — not FragGate live/stub status. Use this when you already have a slug from GET /v1/software or fraggate_list and need the card, not exec. Do not use it for inspecting FragGate live/stub status or executing an op; use fraggate_describe or fraggate_call instead. Not exec — then use fraggate_call. Unknown slug throws unknown product (it does not invent a card and does not refuse FG-HALLUC-TOOL; that code is FragGate-only). Missing skill falls back to in-repo markdown. slug is required. product is an accepted alias of slug. Extra keys besides those two are ignored and are not an op payload. Returns one product card (name, version, skill, download, ops, skill_source).
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  • Run the named DecisionGATE five sequential gates on a proposal (Freedom without clarity is chaos) without executing a catalog product. Also runs automatically inside fraggate_call before exec. Use this when you want a gate check without executing a catalog product verb. Do not use it for executing a product op or searching the library; use fraggate_call or library_lookup instead. Write: appends an ask/refuse ledger tip (not idempotent). Empty {} still runs the five gates and stamps the ledger. Does not execute domain software. Named wrapper — same DecisionGATE kernel; not the full MASTER-33 hop list; Softwares exec stays fraggate_call. All proposal fields are optional. Missing evidence can fail a gate. accountable identity on this runtime is Aziel Eliab only. Mutation requires confirm=true (runtime gate) or dry_run=true (preview only, no write). confirm and dry_run stay optional on inputSchema.required. dry_run still returns FG-HALLUC-TOOL, FG-STUB, FG-UNKNOWN-OP, or FG-LOCAL-ONLY when a confirm call would refuse. confirm is consent, not tenant auth, and it does not upgrade shared public-demo isolation. Returns gate view, final_state, ledger_tip, and result (code FG-OK on the named module wrapper).
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  • Open one hub product card by slug (name, version, skill, download, ops) — not FragGate live/stub status. Use this when you already have a slug from GET /v1/software or fraggate_list and need the card, not exec. Do not use it for inspecting FragGate live/stub status or executing an op; use fraggate_describe or fraggate_call instead. Not exec — then use fraggate_call. Unknown slug throws unknown product (it does not invent a card and does not refuse FG-HALLUC-TOOL; that code is FragGate-only). Missing skill falls back to in-repo markdown. slug is required. product is an accepted alias of slug. Extra keys besides those two are ignored and are not an op payload. Returns one product card (name, version, skill, download, ops, skill_source).
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  • Upload media to the library. Three methods supported: 1. **url** — import from a PUBLIC https URL (e.g. a hosted image link). NEVER pass a local sandbox path — the URL must be reachable from our servers. 2. **base64_data** — pass base64-encoded file data directly. ONLY use this for tiny files (≲50 KB). MCP tool inputs are token-capped — anything larger gets silently truncated and uploads a corrupted file. For any user-pasted image, use upload_url instead. 3. **upload_url** — PREFERRED for any file the user attached to the chat. Call with method="upload_url" to get a one-time presigned URL plus a ready-to-run snippet. Execute the snippet in your code-execution tool against the actual file path. The response JSON contains the media id you pass to create_post. Size limits: base64/direct uploads are capped at **100 MB**. For anything larger — up to **1 GB** — use a public **url** (fetched server-side, bypasses the cap), OR have the user upload the file in the OmniSocials Library UI at https://app.omnisocials.com/library . IMPORTANT: if the user has a large LOCAL file (over ~100 MB) with no public url, do NOT tell them to compress it — point them to the Library UI link above. Large videos (over 100 MB) are processed in the background — the response status is "processing" and the file is NOT usable in a post until it becomes "ready" (re-check with list_media). Compatibility: every upload response includes a "compatibility" summary of any CONNECTED platforms that would reject the file (e.g. too large for Instagram). If there are warnings, RELAY them and ask the user whether to continue before posting — the file still uploads and can post to platforms that accept it. To check BEFORE uploading, call check_media_compatibility first. PDF = carousel: upload a PDF (via a public url, or base64_data with mime_type "application/pdf", or the upload_url snippet) and it is split into one image slide per page (max 20). The response lists a Media ID for EVERY slide — pass ALL of them, in order, as media_ids to create_post to post the deck as a carousel. On LinkedIn the slides post as a native swipeable DOCUMENT made from the ORIGINAL PDF file (the file is kept: text stays sharp, in-document links work, viewers download the real file, and every page is included even past the 20-slide cap) as long as the slides are posted unchanged and in order; on Instagram, TikTok, Threads and Pinterest as an image carousel. Set linkedin.document_source to 'slides' on the post to send a document rebuilt from the slide images instead. This is how a user posts an existing slide deck (Canva/PowerPoint/Figma exported to PDF) as a carousel. Prefer pdf_mode "document" when the user wants ONE library item for the deck (no per-page clutter): the response then has a single Media ID whose media_ids entry expands into every page at post time. Supported: JPEG, PNG, GIF, WebP, MP4, MOV, AVI, PDF. For user-attached files: ALWAYS try upload_url first. Call upload_media with method="upload_url", then in your code-execution sandbox run the returned Python (ChatGPT Code Interpreter, file at /mnt/data/<filename>) or curl (Claude Code Execution) snippet against the actual file path. Only react to a failure AFTER actually executing the snippet.
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  • Get Gonka Network signup link with referral welcome bonus (50M nGNK free tokens). Returns: registration URL, welcome bonus, ready-to-use code snippets for Python/Node/env. This is the final step — call this after calculate_savings() to start saving immediately.
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  • The unit tests (code examples) for HMR. Always call `learn-hmr-basics` and `view-hmr-core-sources` to learn the core functionality before calling this tool. These files are the unit tests for the HMR library, which demonstrate the best practices and common coding patterns of using the library. You should use this tool when you need to write some code using the HMR library (maybe for reactive programming or implementing some integration). The response is identical to the MCP resource with the same name. Only use it once and prefer this tool to that resource if you can choose.
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  • Returns Mastra (Bun) and LangGraph (Python) patterns for AI agent workflows. Call this BEFORE create_workflow / update_draft when building chatbots, tool-using agents, or multi-step LLM flows. Do not hand-roll custom agent loops — use the preinstalled frameworks.
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  • List all 71 available programming languages and their IDs for code execution. Common IDs: 71=Python 3.8, 63=JavaScript (Node.js), 62=Java, 54=C++ (GCC), 60=Go, 73=Rust, 51=C#, 46=Bash. Returns full list with compiler/interpreter versions (Judge0 CE)
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  • Cancel a reservation using the 6-character confirmation code the GUEST received by email/SMS (the code is the authorisation; ask the guest for it — this tool never issues or reveals codes). Thin wrapper over the venue's own cancellation endpoint, which answers uniformly: `status` is "cancelled" or "noop" (unknown code · already cancelled · seated/completed · less than 30 minutes away — it does not say which). Only report a cancellation to the guest when `cancelled` is true. PUBLIC tool, no auth.
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    Destructive
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  • Find which documentation SETS exist whose NAME matches a substring (e.g. "python" → Python 3.x, "react" → React). Returns doc SETS, NOT their content — this does NOT look up a function/method/API name. To search inside a doc for an entry like "Array.map" or "fetch", use search_index (slug + query).
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  • Resolves a package/product name to a Context7-compatible library ID and returns matching libraries. You MUST call this function before 'query-docs' to obtain a valid Context7-compatible library ID UNLESS the user explicitly provides a library ID in the format '/org/project' or '/org/project/version' in their query. Selection Process: 1. Analyze the query to understand what library/package the user is looking for 2. Return the most relevant match based on: - Name similarity to the query (exact matches prioritized) - Description relevance to the query's intent - Documentation coverage (prioritize libraries with higher Code Snippet counts) - Source reputation (consider libraries with High or Medium reputation more authoritative) - Benchmark Score: Quality indicator (100 is the highest score) Response Format: - Return the selected library ID in a clearly marked section - Provide a brief explanation for why this library was chosen - If multiple good matches exist, acknowledge this but proceed with the most relevant one - If no good matches exist, clearly state this and suggest query refinements For ambiguous queries, request clarification before proceeding with a best-guess match. IMPORTANT: Do not call this tool more than 3 times per question. If you cannot find what you need after 3 calls, use the best result you have.
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  • Produce a focused pull-request review checklist for a language or stack. FREE. Covers the things that actually break in production, with extra items per language. Typical input {"language": "python"} returns {"language": "python", "checklist": ["...", ...], "note": "..."}. Use before a review, to decide what to look for. Not for reviewing actual code - pass code to review_diff or security_deep_dive. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
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  • Produce a focused pull-request review checklist for a language or stack. FREE. Covers the things that actually break in production, with extra items per language. Typical input {"language": "python"} returns {"language": "python", "checklist": ["...", ...], "note": "..."}. Use before a review, to decide what to look for. Not for reviewing actual code - pass code to review_diff or security_deep_dive. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
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