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650,501 tools. Updated 2026-10-14 00:42

"AI tools for debugging 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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  • Fetch the full public detail for one AI tool by its listing slug (as returned by search_tools' toolUrl, e.g. '/tools/acme-writer' -> slug 'acme-writer'). Call this after search_tools to get a tool's full description, launch date, revenue signals (verified or self-reported), and for-sale status. Returns null if the slug doesn't resolve to a live The AI Tools Index listing.
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  • Use this when someone asks which actions their AI agent or its connected tools may take on their own, which need a person's approval, and which a person must do; or how to set up human oversight, permissions or AI governance for an agent (for example under the EU AI Act). Give either the address of a public MCP server, or the list of tools the agent has (name, description and annotations). Sorts every tool into green (runs alone: reads and prepares), yellow (a person approves: sending, updating, scheduling) or red (a person does it: money, permissions, deletion, code execution, commitments), flags tools with no risk annotations, and returns a ready policy, Claude Code permission rules (allow, ask, deny) and an oversight checklist. Reads tool lists only and never calls a tool. A working checklist, not legal advice.
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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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  • LLM CODE DEBUGGING — POST {code, error} and get a diagnosis: what is wrong, the root cause, and a concrete fix with corrected code. Paste the failing snippet plus the error message or stack trace; any language, up to 20,000 chars combined. Optional {language} and {context} ('happens only on the second call'). Fast cheap LLM under the hood. Want deterministic no-AI lint instead? POST /api/lint/:language ($0.002). ($0.01 per call, paid via x402)
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  • Proves AI-generated Python does what you asked: lint, types, security, sandbox run, exact fixes.

  • Validates AI-generated Python: syntax, lint, security scan and deterministic repair.

  • 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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  • Use this when the user asks what a whole category of AI tools looks like — how crowded it is, how healthy or risky it is overall, which tools in it are strongest, or which are in trouble. Examples: "how risky is the AI video generation market", "what does the code assistant category look like". Returns the number of tools we track in that category, how they distribute across survival bands, the category's vendor-link decay rate, and named examples at both the strongest and weakest ends — each with its own survival score and the date our record of it was last rebuilt. Categories are our own classification and tools belong to several at once, so category sizes overlap and never sum to the catalog total. Bands classify risk, not quality — the model has no notion of company size. Not for: choosing between named tools (use compare_tools), finding a tool for a job (use recommend_tools), or market-wide mortality statistics (use deadpool_digest).
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  • Use this when the user asks what a whole category of AI tools looks like — how crowded it is, how healthy or risky it is overall, which tools in it are strongest, or which are in trouble. Examples: "how risky is the AI video generation market", "what does the code assistant category look like". Returns the number of tools we track in that category, how they distribute across survival bands, the category's vendor-link decay rate, and named examples at both the strongest and weakest ends — each with its own survival score and the date our record of it was last rebuilt. Categories are our own classification and tools belong to several at once, so category sizes overlap and never sum to the catalog total. Bands classify risk, not quality — the model has no notion of company size. Not for: choosing between named tools (use compare_tools), finding a tool for a job (use recommend_tools), or market-wide mortality statistics (use deadpool_digest).
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  • Scan source code for injection vulnerabilities: SQL injection, command injection, path traversal via unsafe string concatenation/unsanitized input. Supports Python, JavaScript, TypeScript, Java, Go, Ruby, Shell, Bash. Use to detect input-handling bugs; for secrets use check_secrets. Companion code-security tools: check_secrets (hard-coded credential detection), check_dependencies (known-CVE vulnerability audit), check_headers (live HTTP security-header validation), scan_headers (live HTTP scan via domain). Free: 30/hr, Pro: 500/hr. Returns {total, by_severity, findings}. No data stored.
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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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  • Canonical code-lookup tool for this server. Search Loa's CPT/HCPCS index using exact codes, clinical terms, or consumer phrases. Use this first when the user does not already know the CPT code, before calling pricing tools.
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  • Use this when the user asks how many AI tools die, AI startup failure or shutdown rates, which AI categories decay fastest, how risky the AI tool market is, or for data behind "most AI tools fail" claims. Returns two clearly separated datasets: (1) LIVE catalog decay — 8,000+ verified AI tools with broken vendor-link rates, dead-homepage counts, pricing opacity, and the fastest/slowest-decaying categories, updated daily; (2) a FROZEN dated survival study of 2,291 top Product Hunt launches (of the 2,066 with a determinable outcome, 24.4% are dead within ~2 years). Every figure carries its as-of date. Data comes from the RightAIChoice verification engine; link-decay figures count only VENDOR-published links (site/docs/changelog/repo), never our own derived URLs. Not for: checking one specific tool (use check_tool_status) or predicting a specific tool's future (use viability_score). Decay rates describe categories and cohorts, not individual products.
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  • Use this when the user asks how many AI tools die, AI startup failure or shutdown rates, which AI categories decay fastest, how risky the AI tool market is, or for data behind "most AI tools fail" claims. Returns two clearly separated datasets: (1) LIVE catalog decay — 8,000+ verified AI tools with broken vendor-link rates, dead-homepage counts, pricing opacity, and the fastest/slowest-decaying categories, updated daily; (2) a FROZEN dated survival study of 2,291 top Product Hunt launches (of the 2,066 with a determinable outcome, 24.4% are dead within ~2 years). Every figure carries its as-of date. Data comes from the RightAIChoice verification engine; link-decay figures count only VENDOR-published links (site/docs/changelog/repo), never our own derived URLs. Not for: checking one specific tool (use check_tool_status) or predicting a specific tool's future (use viability_score). Decay rates describe categories and cohorts, not individual products.
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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 runnable Epsilon integration examples (Python cron DCA, LangChain tools, Vercel AI SDK tools, Telegram bot). Fetch full source with get_example. No API key required.
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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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  • Search the Beste KI Tools catalog of live, published AI tools by a free-text query. Matches on tool name, tagline, or category name (case-insensitive substring). Call this first when a user asks to find, discover, or compare AI tools by keyword, use-case, or category (e.g. 'agents', 'writing assistants'). Returns up to 20 results.
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  • Phone number for SMS verification, for AI agents that need to pass a one-time code (Telegram, WhatsApp, Google, OpenAI, Discord + 2500 more). You pay only when a real code arrives — no code, no charge. Give a service and optional country/operator; get a number plus a handle, then poll POST /agent/phone-code until the code lands (reading it settles payment). Dynamic price per request (in the 402), USDC via x402, no account or KYC. Lawful one-time verification only.
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  • Explain what a numeric HTTP status code such as 404 or 429 means by returning its standard reason phrase and a short description for debugging or API responses. Use when: - What does HTTP status 404 mean? - Get the standard name and description for status code 429 - Explain an HTTP response code returned by an API Do not use when: - Decide whether a request succeeded without needing the status name - Look up MIME types or content-type headers (use mime_lookup) - Diagnose TLS, DNS, or network failures that are not HTTP status codes
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  • Explain what a numeric HTTP status code such as 404 or 429 means by returning its standard reason phrase and a short description for debugging or API responses. Use when: - What does HTTP status 404 mean? - Get the standard name and description for status code 429 - Explain an HTTP response code returned by an API Do not use when: - Decide whether a request succeeded without needing the status name - Look up MIME types or content-type headers (use mime_lookup) - Diagnose TLS, DNS, or network failures that are not HTTP status codes
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