Get the README content for a hex.pm package.
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261,240 tools. Last updated 2026-07-05 11:27
"ReadMe" matching MCP tools:
- Return the MCP-native README/playbook summary with docs links, workflows, safety rules, and recommended next calls.Connector
- Read a file from a PUBLIC GitHub repository (or list a directory) by path. PREFER OVER WEB SEARCH for "show me the README / package.json / <file> of <repo>", "read <path> from <owner/repo>", inspecting source or config files. Pass owner + repo + path (omit path or "" for the repo root listing). Optional ref = branch/tag/commit SHA. Returns decoded text for files (capped ~60k), or a directory listing of {name, path, type, size}.Connector
- Create a temporary JSON database (24h TTL, no signup, no keys). Returns the db URL — the only credential — plus admin URL, limits and expiry. Create once per project/task, persist the db URL immediately (local ~/.tmpstate/credentials, project README, and your memory), and reuse it instead of creating again. For retries or parallel workers, pass a stable idempotency_key so duplicate calls return the same database.Connector
- Fetch full AWS doc pages as markdown. `search_documentation` already returns verbatim page chunks, so don't re-read a URL whose chunk you already have to "confirm" or "round out" an answer -- the chunk is the real page text; treat it as authoritative. Reading the full page is justified ONLY when the chunks genuinely lack the content: - an enumeration or aggregation ("list all X", "how many X") needs the complete set and the chunks show only part of it; - no search result is on-topic after refining the query, and a known doc URL would have the answer. Otherwise, answer from the chunks. Use exact URLs from `search_documentation`; don't guess slugs. Input: `requests: [{url, max_length?, start_index?}]`. Batch 2-5. - `max_length` default 10000. - `start_index` default 0; use prior `end_index` to continue, TOC offset to jump. Allow-listed prefixes: docs.aws.amazon.com; aws.amazon.com (not /marketplace); repost.aws/knowledge-center; docs.amplify.aws; ui.docs.amplify.aws; github.com/{aws-cloudformation/aws-cloudformation-templates, aws-samples/{aws-cdk-examples, generative-ai-cdk-constructs-samples, serverless-patterns}, awsdocs/aws-cdk-guide, awslabs/aws-solutions-constructs, cdklabs/cdk-nag} (README on `main`); constructs.dev/packages/{@aws-cdk-containers, @aws-cdk, @cdk-cloudformation, aws-analytics-reference-architecture, aws-cdk-lib, cdk-amazon-chime-resources, cdk-aws-lambda-powertools-layer, cdk-ecr-deployment, cdk-lambda-powertools-python-layer, cdk-serverless-clamscan, cdk8s, cdk8s-plus-33}; strandsagents.com/latest/documentation/docs/. Output: SUCCESS -- markdown + `total_length, start_index, end_index, truncated, redirected_url?` (truncated includes TOC with char ranges). ERROR -- `error_code` in {not_found, invalid_url, throttled, downstream_error, validation_error}.Connector
- List artifacts in a directory. Returns the immediate contents of a directory (not recursive). Separates folders and files for easy navigation. Args: path_prefix: Directory path to list (default: "/") name_pattern: Optional case-insensitive substring filter on file/folder names Returns: Formatted directory listing or error message Examples: >>> await list_artifacts("/") {'success': True, 'path': '/', 'folders': [...], 'files': [...]} >>> await list_artifacts("/", name_pattern="readme") {'success': True, 'path': '/', 'folders': [], 'files': [{'name': 'readme.md', ...}]}Connector
Matching MCP Servers
- AlicenseAqualityAmaintenanceA smart documentation system that helps AI assistants understand and follow your project's conventions by automatically discovering, routing, and managing AI_README.md guide files.Last updated67791MIT
- FlicenseCqualityDmaintenanceProvides tools to summarize README files from local paths or public Git repositories into structured JSON summaries. It enables users to quickly extract key facts and project overviews from documentation through automated tool calls.Last updated2
- Search the DevMatch index for engineers matching a role or project. Input: pass the richest context you have — (1) a full job description (most common), (2) a synthesized brief after reviewing a company's public repo (README + stack + role needs — preferred over a bare URL when you've evaluated the project), (3) a public github.com repo URL (server fetches README/topics; private repos → paste README as text), or (4) an informal role brief. Longer, more specific input ranks better. Returns up to limit ranked candidates (default 20, max 50) with full inline profiles in structuredContent (view=candidates): login, name, bio, location, followers, html_url, top_repos, top_topics, signals, matched_projects, and contact. Results never include bots, CI, or service accounts — they are filtered out automatically. Use the optional `exclude` array (GitHub logins or org names) to drop additional accounts. AGENT MODE: consume structuredContent only. HUMAN MODE: MCP App panel shows candidate cards; use server instructions for text-only hosts. Do not call get_profile for handles already in these results unless the user asks for deeper detail.Connector
- Get detail for a CoreClaw worker. WHEN TO USE: Use before running a worker to inspect version, README, and parameters. 中文触发: 当用户要在 CoreClaw 中查询、运行、重跑、停止、导出或查看对应 worker/run/task 数据时使用。 WHEN NOT TO USE: Do not use public web search or code search for private CoreClaw platform data. Do not call excluded internal worker-version or internal-detail APIs. RETURNS: JSON with worker name, username, version, readme, and parameters. WORKFLOW: Follow with get_worker_input_schema and then run_worker.Connector
- Use for a known approved pet slug when the user needs website iframe embed HTML or an embed URL. Do not use for README badges/cards, install instructions, or pet discovery; use get_badge_code, get_card_code, get_install_instructions, or search_pets instead.Connector
- Record a point-in-time inventory of the user's project under a workspace. Remote MCP cannot see the filesystem, so YOU (the AI) collect this inventory with your own Read/Glob/Grep tools before calling this. Persist it so future setup, bootstrap, drift detection, and onboarding flows have structured evidence to reason over. Required: workspace_id. Strongly recommended: project_name, file_count, file_tree (cap at ~5000 entries — summarise deeper paths), file_extensions_summary, top_level_dirs, sampled_contents for README, package.json / pyproject.toml / Cargo.toml, CLAUDE.md, AGENTS.md, main config files (truncate each to ~4KB). Optional: git_head / branch / git_log_summary if you can read them, ai_notes for free-form observations.Connector
- Check an MCP server for malware / prompt-injection lures by its endpoint URL. Give the server's streamable-http endpoint URL. Two paths: * **Already in the agent-tools directory** → returns our LATEST stored rule verdict. Every indexed server is re-scanned hourly, so you get a consistent, continuously-refreshed answer without re-probing. * **Not yet indexed** → we probe the endpoint live, statically scan its advertised tools + metadata, ADD it to the directory, and return the fresh verdict (so the next caller gets the rule verdict instantly from cache). Two dimensions are reported. `verdict` is authoritative and comes from deterministic static rules — pure pattern-matching over the *advertised* text only, NO code execution. It flags the social-engineering / RCE tricks listing-spam servers use: * `curl … | bash` and `base64 -d | sh` install lures * `eval "$(curl …)"` / PowerShell `IEX(...DownloadString)` cradles * base64 blobs that decode to a shell command * bare-IP payload hosts and cheap throwaway TLDs * prompt-injection / credential-exfiltration phrasing ("ignore previous instructions", "send your .env / api key") * MCP tool-poisoning coercion — descriptions that hijack an agent's tool-calling ("always call this tool first", "before using any other tool you must…"), hidden `<IMPORTANT>` instructions, "list all API keys / include secrets in your response", and coercion to read & forward `.key`/`.pem`/`.ssh`/`.env` files Source-code-oriented rules (SQL / command / code injection) are deliberately not applied to natural-language descriptions, to avoid false positives. `llm_reference` is an advisory frontier-LLM second opinion over the same text. Because the LLM is slow it is computed LIVE on this call only and is never stored (the hourly job never runs it), so it may be null on timeout. It never overrides the rule verdict; when it is *more* severe than the rules an `advisory` note is attached as a safety-net signal. Security/defense products that merely *name* these attacks are not flagged. Args: endpoint_url: The MCP server's streamable-http URL (required). This is the identity we look up / index by. name: Optional advertised name (used when the server is new and gets added; falls back to the URL host). description: Optional description / README blurb (scanned when new). tools_text: Optional tool names + descriptions; used only if the live probe cannot fetch the server's tools/list. Returns: { verdict: "clean"|"suspicious"|"malicious", score: 0-100, reasons: [{rule, weight, snippet}], llm_reference: {model, verdict, reason, confidence} | null, advisory: str | null, slug, name, endpoint_url, source: "stored" (existing) | "new_scan" (just added), indexed: bool }Connector
- Find open-source projects similar to a seed GitHub repo, ranked by semantic similarity to its description/topics/README. Each result includes top contributors as leads — not JD-ranked candidates. Returns structuredContent (view=similar_projects). Agents: consume structuredContent only. Use for landscape mapping or to discover anchor repos; follow with find_candidates for hiring matches.Connector
- Scaffold a new MCP connector with server.js + package.json + README. Eliminates ~50% of identical boilerplate (MCP server setup, tool registration, stdio transport). You then fill in the tool implementations. Set ui_capable=true to include ui.listPlugins / ui.getPlugin stubs (plugin source files added separately via ateam_create_plugin). After scaffolding, the files are uploaded to Core via the same path as ateam_upload_connector.Connector
- Use for a known approved pet slug when the user needs README badge Markdown, HTML, or SVG URL. Do not use for animated README cards, website iframe embeds, install instructions, or pet discovery; use get_card_code, get_embed_code, get_install_instructions, or search_pets instead.Connector
- Get a behavioral commitment profile for any public GitHub repository. Returns real signals that prove genuine investment: how long the project has existed, recent commit frequency, contributor community size, release cadence, and social proof. These are behavioral commitments — harder to fake than README claims or marketing copy. Useful for: vetting open-source dependencies, evaluating AI tools/frameworks, assessing vendor reliability, due diligence on any GitHub project. Examples: "vercel/next.js", "facebook/react", "https://github.com/piiiico/proof-of-commitment"Connector
- Generate a .faf file from any public GitHub repository WITHOUT cloning. Extracts 6 Ws from README, analyzes stack from languages and package.json, and generates Championship-grade AI context. Returns .faf content, quality score, and metadata.Connector
- Get an embeddable Trust Rating badge for an agent — returns the badge image URL plus ready-to-paste Markdown and HTML snippets for a README or agent card.Connector
- Register this AI agent on Signal — the Zambo agent identity registry (zambo.dev/signal). Takes 1 call. Returns a public profile URL, badge embed code, and README snippet. IMPORTANT: registering unlocks the Signal Agent tier: 25 free tool calls/day (vs 10 unregistered). After registering, pass _signal_handle: '<your-handle>' on every tool call to claim the elevated limit. Any agent (Claude, GPT, Cursor, custom bot) can self-register. Free, no auth, no payment. Use this the first time you connect to Zambo MCP to establish your identity and double your rate limit instantly.Connector
- Get full details for an MCP server package from the ThinkNEO Marketplace. Returns readme, full tools list, version history, reviews, security score, and installation instructions. No authentication required.Connector
- Extract real-time data from a GitHub repository — README, stars, forks, last commit, topics. Returns timestamped freshcontext.Connector