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728,529 tools. Updated 2026-10-31 11:31

"A server for finding coding resources and programming information" matching MCP tools:

  • Maps only stable Tier1 finding identifiers to approved Tier1 services and public resources. Call after a Tier1 score or email-domain check. Do not submit prose, URLs, customer information, or invented identifiers. This tool performs no arbitrary fetching, makes no contact request, changes nothing, and stores nothing.
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  • Lints one DESIGN.md file with the official @google/design.md 0.4.0 linter, the same check as `npx @google/design.md lint DESIGN.md`. Best for: checking a DESIGN.md file the user pasted before it goes to a coding agent. Pass the whole file contents in text, including the YAML front matter between the --- lines; a file path or URL will not work, so ask the user to paste the file. Returns: each finding with its severity, rule and token path (broken token references, low text contrast, missing primary color, unused colors, section order, unknown keys), the error, warning and note counts, the sections found and the token counts. The file is valid when it has no errors. Front matter that is not valid YAML comes back as a finding, not an error. Do not use for: comparing two versions (use diff_design_md) or reading a website (use extract_design_md).
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  • Returns the canonical guide for using TMV from a coding-agent context. Covers the fix-test-retest loop, how to write a good test prompt, how to read the actionTrail / consoleErrors / failedRequests outputs, and common gotchas. Call this first if you're a new agent on a project — it'll save you a debug session. The same content is served at https://testmyvibes.com/docs/coding-agents.
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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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  • Get remediation advice for a single finding as GitHub-flavored markdown. When AI Assist is enabled and within budget this is a suggestion written for this exact finding; otherwise it falls back to the static guidance-library text and says so in 'source'. Unlike the other reads this one can spend AI budget, which is why it is a separate tool. Use this for advice on one finding; for the whole library use list_security_guidance. Requires project context.
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  • Creates a DRAFT campaign from a sequence. With a LinkedIn Sales Navigator search it also starts finding + verifying leads for it; without one the draft holds the emails and leads can be added from Lead Finder or a CSV upload. Nothing sends — the campaign stays a draft until you review and launch it. Returns the new campaign id, plus a lead-finding job id when lead finding started.
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  • Retract support for a finding you authored, with a reason. Original content remains visible and labeled withdrawn. Identical retries are safe; this does not claim the finding is disproven.
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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 INDEPENDENT Marginalia web index — a separate crawler that favours text-heavy, ad-light, non-commercial pages: personal sites, technical writing, documentation, forums, academic and hobbyist pages. Use it when mainstream search results are dominated by SEO content, listicles, and marketing pages, or when you want a second opinion from an index that is NOT Google/Bing-derived (unlike serper_search / exa_search / serpapi). Especially good for programming and niche technical topics, obscure or historical subjects, and finding primary/hobbyist sources. Returns url, title, description, a quality score, and how many other results share the domain. Weak for breaking news, e-commerce, and mainstream/celebrity topics — use serper_search for those. Results are CC-BY-NC-SA 4.0 licensed; the licence is returned with every response.
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  • Return a list of hands-on SecDim Play secure coding challenges (labs) related to a detected or suspected vulnerability. SecDim Play challenges are scored, hands-on labs: find and fix a real vulnerability in running code to earn points and badges. Use this tool to: - Find hands-on SecDim Play labs for specific vulnerabilities like XSS, SQL Injection, etc. - Explore OWASP Top 10 vulnerabilities and related labs - Provide additional resources and guides to help developers improve their secure coding skills For structured tutorial content (text, video, and lab-based courses) on the same vulnerability, use search_learn_courses (SecDim Learn) instead or in addition. Args: search: Search term for the vulnerability (e.g., 'xss', 'sql-injection', 'injection') cwe: Common Weakness Enumeration (CWE) ID to filter by owasp: OWASP category to filter by (e.g., 'a03:2021') technology: Technology or framework to filter by (e.g., 'react', 'django') language: Programming language to filter by (e.g., 'javascript', 'python') difficulty: Difficulty level to filter by (e.g., 'trivial', 'easy', 'medium', 'hard') type: Challenge format to filter by (e.g., 'battle', 'exploitation', 'incident-response') mitre: MITRE ATT&CK ID to filter by (e.g., 'T1102.003') SecDim Play challenges (labs) each simulate a real vulnerability. They are scored according to the following difficulty levels: - Trivial: Easy to find and path vulnerabilities. It can be completed in 5-10 minutes. 1-15 points. - Easy: Known vulnerabilities. It can be completed in 10-30 minutes. 16-35 points. - Medium: Known vulnerabilities but require defence-in-depth patch. It can be completed in 20-30 minutes. 36-70 points. - Hard: Hard to find or patch vulnerabilities. It can be completed in 30-60 minutes. 71-100 points. - Battle: SecDim Flagship attack and defence challenge that require both vulnerability exploitation and mitigation skills. Points are accumulated. Returns: Dictionary containing SecDim Play labs results or error If there are no results, user can perform a manual search on the SecDim Play frontend (SECDIM_PLAY_FRONTEND_BASE_URL)
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  • Retrieve the full metadata record for a specific LOC digital item. Returns the imprint (place, publisher, and date of publication), contributors, LCSH subject headings and the subject terms the search subject filter accepts, summary, languages, locations, rights information, physical description, call number, original and online formats, access restrictions, former catalog IDs, notes, and related items, plus the item's digital resources (each with its page count, representative image, PDF, and audio) and a window of its pages. Each page has a page_url and has_text; pass a page_url with has_text true to libofcongress_get_newspaper_page to read that page's text (newspaper, book, and manuscript pages alike). A long item lists 50 pages per call: continue with page_offset set to next_page_offset. Set include_files for each listed page's downloadable files (TIFF, JP2, JPEG sizes, text). Use after libofcongress_search_items on a result whose is_item is true. Pass the result id — a simple ID, an LCCN, or a slash-separated newspaper path — or a loc.gov item link (`https://www.loc.gov/item/<id>/`) or LCCN permalink (`https://lccn.loc.gov/<lccn>`), which is read as its id. Non-item results (is_item: false) — collections, exhibits, guides, newspaper pages, catalog.loc.gov records, hdl.loc.gov finding aids — have no item record and cannot be retrieved here.
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  • Use when the user asks how much revenue, MRR or ARR an AI agent, AI startup or AI tool makes, or wants real examples of AI businesses in a niche (support, coding, voice, sales, automation...). Returns AI-agent businesses with sourced, dated revenue (start here). Example: {"query":"coding","limit":3} -> 3 coding agents with their sourced revenue. Use it to find or filter businesses (keyword, category, tier, verified_only) when you have no slug; for a pure top-N use leaderboard, for one full record get_business. Each brief: slug, name, category, tier, one_liner, annualized_revenue_usd, basis, confidence, verification_label ("reported, not payment-verified" unless processor-verified), url_profile; + total + next page (offset/cursor, limit max 50). Empty query = all, highest revenue first. Cost: free (100 calls/day without a key).
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  • Use when the user asks which AI agents or AI startups make the most money, or for a top-N revenue ranking. Returns businesses ranked by latest sourced annualized revenue, highest first (same briefs, with verification_label, as search_businesses). Example: {"category":"coding-agents","limit":5} -> the top 5 coding agents by revenue. Use it for a pure ranking or top-N, optionally per category or verified_only (limit max 50); it has no keyword search, use search_businesses for that. Cost: free (100 calls/day without a key).
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  • Analyzes combined multimodal prompts spanning text, image descriptors, audio transcripts, and video temporal events using Gemini Omni Flash. Use when correlating information across multiple media modalities in a single reasoning pass; for running pure text-only reasoning or coding prompts, use generate_gemini_completion instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'input' ('modality_focus' defaults to 'cross_modal_synthesis'), supports '?demo=true' or L402 (100 sats), and returns HTTP 400 if invalid.
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  • Analyzes combined multimodal prompts spanning text, image descriptors, audio transcripts, and video temporal events using Gemini Omni Flash. Use when correlating information across multiple media modalities in a single reasoning pass; for running pure text-only reasoning or coding prompts, use generate_gemini_completion instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'input' ('modality_focus' defaults to 'cross_modal_synthesis'), supports '?demo=true' or L402 (100 sats), and returns HTTP 400 if invalid.
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  • Add one bank account's transactions to the coding session. ★ This is where credits are charged: four a transaction, as the rows go in, not at run_coding, so tell the user the price before this call. Call again for each further account, then run_coding once. A repeat call for the same account adds rows and never replaces them, so a long statement can go in batches without resending. Add every account before coding: transfers between accounts are only found when both are present.
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    Destructive
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  • Pull the company's unpaid sales invoices and bills from the accounting platform into this coding session, refreshing them if they are stale. Do this before run_coding whenever the period contains customer receipts or supplier payments: it is what lets the pipeline recognise a payment as settling a specific invoice rather than coding it as general income or expense.
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  • Get the live status of AI coding providers (Claude, OpenAI, Copilot, Cursor, OpenRouter and more). Returns status, how long each provider has been in its current state, and last check time. Pass service_id for one provider, omit it for all.
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  • Render a ready-to-paste coding prompt for an annotation, formatted for a target assistant. A deterministic template (no AI spend); for a deeper analysis use diagnose_annotation.
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  • Score how bot-friendly a website is (0-100). Fetches the site the way an agent would — no JavaScript, no cookies — and checks access (reachability, HTTPS, robots.txt, AI crawlers allowed, real 404s), content without JavaScript (H1, 500+ chars of raw text, metadata, JSON-LD), and machine discovery (llms.txt, sitemap, OpenAPI spec or ai-plugin.json), with evidence and fix advice per check. Set format to 'prompt' to instead get a ready-to-paste coding-agent prompt that fixes every failed finding. Rate limited to 6 checks per minute per caller; each check takes a few seconds.
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  • Writes a guide to finding a house church, microchurch, dinner church, Alpha course, or church small group to join or start, tailored to what the user has shared. Use when the user asks for help finding a Christian gathering or community, whether after leaving a church, moving, coming to faith, or being between churches. Inputs, all optional: area, faith tradition, life stage, in-person or online, what they want to avoid, and whether they want to join or start a group. Output: where people from that tradition typically look (for example parish groups, denominational networks, house-church directories, Alpha), a checklist of warning signs of high-control groups and signs of a healthy one, and a link to Sanctuary's finder page, where a person can ask for an introduction. It does not search a database of groups, does not return specific groups, addresses, or contact details, and does not assess or comment on any named church or group. If any field describes suicide or self-harm, it returns crisis resources (988 and Crisis Text Line) instead.
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