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457,697 tools. Updated 2026-08-14 14:35

"AI tools for debugging Python code" matching MCP tools:

  • Find working SOURCE CODE examples from 37 indexed Senzing GitHub repositories. REQUIRED: either `query` (string, for search) or `repo` with `file_path` or `list_files=true` — the call WILL FAIL without one. Three modes: (1) Search: pass `query` to find examples across all repos, (2) File listing: pass `repo` + `list_files=true`, (3) File retrieval: pass `repo` + `file_path`. Indexes source code (.py, .java, .cs, .rs) and READMEs — NOT build/data files. For sample data, use get_sample_data. Covers Python, Java, C#, Rust SDK patterns: initialization, ingestion, search, redo, configuration, message queues, REST APIs. Use max_lines to limit large files. Returns GitHub raw URLs for file retrieval.
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  • Score a URL for design-system AI readiness — the 6th maturity axis (zeroheight 2026). 10 checks probe the target origin for machine-readable artifacts: DTCG token files, llms.txt, agent.json, MCP endpoint (tools/list), DESIGN.md, token $description, component schemas, sitemap.xml, robots.txt, and Open Graph/Twitter meta. Use this to verify whether a design system is the default context AI tools build from, or whether AI is silently working around it. When NOT to use: for full design-contract scoring, use designesy_score; for AI-drift detection, use designesy_drift_score. Executable — fetches the URL and probes the origin via HEAD/GET for each artifact. No browser needed. Returns JSON: { ok, url, score (0-100), grade (A-F), pass, warn, fail, total, checks[{id, item, category, status, detail}] }. Results cached ~24h per URL.
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  • 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. 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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  • Returns runnable code that creates a Solana keypair. Solentic cannot generate the keypair for you and never sees the private key — generation must happen wherever you run code (the agent process, a code-interpreter tool, a Python/Node sandbox, the user's shell). The response includes the snippet ready to execute. After running it, fund the resulting publicKey and call the `stake` tool with {walletAddress, secretKey, amountSol} to stake in one call.
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  • Render a mingrammer/diagrams Python snippet to PNG and return the image. The code must be a complete Python script using `from diagrams import ...` imports and a `with Diagram(...)` context manager block. Use search_nodes to verify node names and get correct import paths before writing code. Read the diagrams://reference/diagram, diagrams://reference/edge, and diagrams://reference/cluster resources for constructor options and usage examples. Args: code: Full Python code using the diagrams library. filename: Output filename without extension. format: Output format — ``"png"`` (default), ``"svg"``, or ``"pdf"``. download_link: If True, return a temporary download URL path (/images/{token}) that expires after 15 minutes; if False, return inline image bytes. Defaults to True (URL) — set ``DIAGRAMS_INLINE_DEFAULT=true`` on the server to flip the default. SVG/PDF and PNGs larger than the inline limit always use a download link.
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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.

  • Return AI-assistant (ChatGPT/Claude/Perplexity/Gemini/Copilot) traffic for the given period. mode='referred' (default) lists landing pages that received clicked AI traffic — per page × AI source: sessions, bounce rate (%, always computed; judge reliability via the sessions count), summed revenue, and last citation date (default limit 100); a view GA4/GSC cannot produce (GSC is Google-search only; GA4 lacks an AI-source breakdown). mode='gaps' returns where the site leaves AI value on the table as a ranked action list: (1) missed_citation_pages — content articles with real audience but ~0 AI traffic (push for AI citation / GEO), ranked by engagement-weighted reach; (2) under_monetized_ai_pages — pages WITH AI traffic engaging below the site's own AI norm (improve landing/CTA), ranked by AI arrivals lost below benchmark (default limit 10/list); methodology fixed in code. site_id is OPTIONAL when OAuth-authenticated. Default period is the last 30 days; pass period='today'/'7d'/'90d' or a raw day count (1-365). Scope is clicked citations only.
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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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  • Get the active pricing model for this operator. Free. If no model exists, self-initializes a scaffold with all registered tools at 0 sats. No economic data from code.
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  • Analyze any image using AI vision for manual inspection, debugging, visual description, or supplemental critique. Provide exactly one source: generation_result_id for a Shoot Board generation, uploaded_file_id for a Files item, or image_url for a public HTTPS image. Do not use this as the primary QA mechanism when the user asks to QA, quality-check, validate, review, approve/reject, or assess generated results; for QA requests use queue_generation_result_qa first, then read_generation_result_qa.
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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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  • 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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  • Re-deploy skills WITHOUT changing any definitions. ⚠️ HEAVY OPERATION: regenerates MCP servers (Python code) for every skill, pushes each to A-Team Core, restarts connectors, and verifies tool discovery. Takes 30-120s depending on skill count. Use after connector restarts, Core hiccups, or stale state. For incremental changes, prefer ateam_patch (which updates + redeploys in one step).
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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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  • Get the active pricing model for this operator. Free. If no model exists, self-initializes a scaffold with all registered tools at 0 sats. No economic data from code.
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  • Lightweight catalogue of all registered frameworks — one row per framework with framework_id + intent + 1-line applies_when + version. Useful for discovery / debugging without parsing the full library. For the actual decision template, call tengu_v3_framework_lookup.
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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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  • Identify what a DNS top-level domain such as com, io, or ai is classified as and commonly used for when classifying domains or answering TLD questions. Use when: - What kind of TLD is .io? - Is .ai a country-code or generic top-level domain? - Get curated metadata for DNS TLD com Do not use when: - Check whether a full domain name is registered or available - Resolve DNS records or WHOIS ownership - Look up MIME types for file extensions (use mime_lookup)
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  • Scan source code (or snippet) for hardcoded secrets — cloud provider keys, API tokens, connection strings, private keys, passwords. Supports Python, JavaScript, TypeScript, Java, Go, Ruby, Shell, Bash. Use to detect leaked credentials before commit; for injection detection use check_injection. Free: 30/hr, Pro: 500/hr. Returns {total, by_severity, findings}. No data stored. The generic password-assignment rule is suppressed when a more-specific credential rule fires on the same line — one targeted finding per leaked secret, not two.
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