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305,560 tools. Last updated 2026-07-23 07:10
"An MCP for extracting and analyzing documentation using AI" matching MCP tools:
- Retrieve reference documentation for the Zaira Guide API and MCP server on demand. Topics: - getting_started — how to connect via MCP or REST, first queries - endpoints — full REST endpoint reference with parameters - mcp_tools — MCP tool reference with when-to-use guidance and a routing matrix - schema — the tool entry schema - errors — error taxonomy for REST (RFC 9457) and MCP (JSON-RPC) Call with no topic to get an index of available topics. Returns: the requested topic as a Markdown-KV block. With no topic, returns an index listing all available topics with short descriptions; call again with the relevant topic for the full content. Examples (topic selection): - "How do I call the REST API?" → {topic: "getting_started"} - "What parameters does /tools accept?" → {topic: "endpoints"} - "What fields are in a tool entry?" → {topic: "schema"} - "What error shapes do I handle, and what are the recovery steps?" → {topic: "errors"} - "Which MCP tool fits my task?" → {topic: "mcp_tools"} Edge cases: - No topic argument is valid — you get the index. This is the deferred-loading path; don't load every topic at once. - Topic must match the enum exactly (lowercase, underscore). "getting-started" with a hyphen is rejected as an unknown parameter. Risk: read-only, closed-world, idempotent — no state change possible.Connector
- AI-powered company analysis using semantic search over Nordic financial data. Orchestrates multiple searches internally and returns a synthesized narrative answer with source citations. Covers annual reports, quarterly reports, press releases and macroeconomic context for Nordic listed companies. Use this when you want a synthesized answer rather than raw search chunks. For raw data access, use search_filings or company_research instead. For a full due diligence report with AI-planned sections, use the Alfred MCP server: alfred.aidatanorge.no/mcp Args: company: Company name or ticker question: What you want to know about the company model: 'haiku' (default) or 'sonnet'Connector
- Probes a domain for known AI agent integration signals: `llms.txt`, `ai.txt`, `/.well-known/ai-plugin.json`, `openapi.json`, `swagger.json`, MCP manifest, MCP SSE endpoint. Returns a score based on the count of signals detected. Use this to assess whether a domain is ready for agent-to-agent interaction. Use this tool when: - You want to know whether a domain exposes an MCP server or OpenAPI spec for agents. - You are cataloguing the AI-agent-ready surface of a set of domains. - You need to decide whether to attempt programmatic API access to a domain. Do NOT use this tool when: - You need tracker/surveillance data about the domain — use `get_domain` instead. - You need the robots.txt AI crawler policy — use `intel_robots` instead. - You need HTTP security posture — use `intel_http` instead. Inputs: - `domain` (query, required): Domain to probe. Returns: - Boolean flags per signal (`llms_txt`, `ai_plugin`, `openapi`, `mcp_manifest`, `mcp_endpoint`, `mcp_sse`). - `agent_surface_score`: integer 0-8, count of signals detected. Cost: - Free. No API key required. Latency: - Typical: 2-5s (parallel probes), p99: 8s.Connector
- Keyword and semantic search across the connected repository's generated docs, conventions, documentation gaps, AI-context notes, and indexed code. Read-only; no side effects. Returns ranked matches in Markdown grouped into Documentation and Code sections, each with a title, snippet, and source paths. Use for open-ended lookups when you don't know which category holds the answer; when you do, the specific getters (get_conventions, get_doc_gaps, get_documentation_opportunities) are more direct. Omitting query returns recent context instead.Connector
- REST API access for autonomous agents — pricing, quick start, and migration guide. Call this when: building a trading bot, deploying an autonomous agent, hitting the MCP rate limit, or running 24/7 without a human in the loop. The MCP tier (what you're using now) is free via Smithery, rate-limited to 60 calls/minute per IP, and good for testing. The REST API is for production: pay per call in USDC; paid endpoints are rate-limited to 60 calls/minute and 200 calls/hour per wallet. No API key required.Connector
Matching MCP Servers
- Alicense-qualityDmaintenanceProvides AI assistants with a standardized interface to interact with the Todo for AI task management system. It enables users to retrieve project tasks, create new entries, and submit completion feedback through natural language.Last updated9Apache 2.0
- AlicenseAqualityAmaintenance50 tools and 400 functions for working with Excel/.xlsx spreadsheets — read/write, recalculate formulas, diff, repair broken references, and audit. Built for AI agents.Last updated508064MIT
Matching MCP Connectors
MCP server for Vonage API documentation, code snippets, tutorials, and troubleshooting.
Honeydew AI Documentation MCP — semantic search and ripgrep-grade filesystem queries over Honeydew AI docs and OpenAPI specs, for AI coding agents.
- Analyze text for writing style issues: weasel words, passive voice, duplicate words, long sentences, nominalizations, hedging, filler adverbs, and research-cited AI tells. Read-only and stateless — text is analyzed in memory on the hosted server and never stored. Returns a plain-text report with each issue's line and column, the matched text, surrounding context, and the reason for AI tells; texts over 100,000 characters return an error message. This hosted server has no filesystem access — the wsc-mcp npm package adds a check_file tool for local files. It only reports issues — to auto-remove duplicate words, follow up with fix_duplicates.Connector
- Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework.Connector
- Use when conducting an AI risk management gap assessment, building board-level AI governance documentation, preparing for a model risk examination, or aligning an AI program with federal regulatory expectations. NIST AI RMF 1.0 is the US federal standard for AI risk management — adopted by reference in the Executive Order on Safe AI and aligned with Federal Reserve SR 26-2, OCC model risk guidance, and FDIC requirements. Returns all four functions (GOVERN, MAP, MEASURE, MANAGE) with categories, subcategories, and implementation guidance. Example: GOVERN function requires board-level AI policy, documented accountability structures, and AI risk culture assessment — the first control examiners check in a model risk review. Source: NIST AI RMF 1.0.Connector
- FREE, no payment required. Instant trust check of any MCP server: returns only the 0-100 score, A-F grade, tool count, latency and a one-line verdict — no detailed report. Use this FIRST, before integrating any third-party MCP server, to see at a glance whether it is technically trustworthy; an unreliable MCP wastes your tokens and can break your workflow. For the full actionable report (per-tool documentation coverage, functional probe results, score breakdown, plain-language summary) call evaluate_mcp; to pick between alternatives call compare_mcps. Set 'url' (required) to the target's MCP endpoint (Streamable HTTP), e.g. https://host/mcp.Connector
- Use this when the user explicitly wants to delete a whole documentation Space. Destructive state-changing action: removes the manageable Space directly after the MCP client authorizes the call. Requires numeric space_id from list-spaces and should not be used for deleting individual pages.Connector
- Get Lenny Zeltser's Malware cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `malware_load_context`. This server never requests your sample, analysis notes, or indicators and instructs your AI to keep them local—guidelines and the report template flow to your AI for local analysis.Connector
- Get Lenny Zeltser's Security Assessment cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `assessment_load_context`. This server never requests your assessment notes or report and instructs your AI to keep them local—the templates and guidelines flow to your AI for local analysis.Connector
- Log in to an existing AI Note account and return an MCP API key. No prior authentication required. SIDE EFFECT: if the user has no MCP key yet, this call creates one (write to user.mcp_keys), so it is NOT idempotent and must be gated like other key-creation flows.Connector
- Check the status of a submitted job. Call this after submit_query to see if your job is ready. Status progression: submitted -> analyzing -> fetching -> clustering -> enriching -> completed/failed IMPORTANT: Jobs take several minutes to process. First check after ~1-2 minutes, then poll every 30-60 seconds. Broad searches can take 10-30+ minutes; for long jobs, poll every 60-120 seconds. Do NOT call this tool in a tight loop. Stop polling when status is `completed` or `failed`. Treat `submitted`, `analyzing`, `fetching`, `clustering`, and `enriching` as active states and continue polling. You don't need to wait for completion to pull results. Partial results are available during `enriching` — call pull_results after ~2 minutes, then poll status every 30-60 seconds and pull again for fresher results. Do not stop pulling just because an intermediate pull is empty/unchanged. Use `progress_validated` vs `candidate_records` to track whether more results may still appear (`progress_validated < candidate_records`). If transport/session fails, resume using the same `job_id`.Connector
- Permanently revoke one of your Integration API keys. Any MCP clients or integrations using the key will lose access immediately and cannot be restored. Returns a preview; re-call with the confirm_token and an idempotency_key to commit.Connector
- DEV ONLY — Sign and broadcast an unsigned transaction using a local private key (PK env var). For production, use a dedicated wallet MCP server (Fireblocks, Safe, Turnkey, etc.) instead of this tool. Takes the transaction object returned by any write.* tool and submits it onchain.Connector
- Public mode returns FS AI RMF framework reference data only — not org-specific scoring. Use when assessing an organization FS AI RMF governance maturity stage or preparing a regulatory AI roadmap presentation. Returns INITIAL, MINIMAL, EVOLVING, or EMBEDDED classification with stage criteria and remediation priorities. Example: EVOLVING stage organizations have documented AI policies but lack systematic model validation — typical gap to EMBEDDED is 18-24 months and 12-15 additional controls. Connect org MCP for org-specific scoring. Source: FS AI Risk Management Framework.Connector
- Get the AI Defense Matrix evaluation playbook for assessing an AI security program: per-cell prompts, gap-inventory template, and a workflow that walks each asset class first and rolls findings up to the Govern column. Supports mode='gate' for binary deployment-gate decisions (returns the deployment-gate workflow plus gate-tier prompts only) and consumerPattern for scoping to consumed-vs-built AI deployments. The AI applies these prompts against your program documentation locally, and no program details leave your client. This server never requests your program docs or product roadmap and instructs your AI to keep them local—the matrix, framework alignments, and playbooks flow to your AI for local analysis.Connector
- Test a regular expression pattern against an input string and return all matches with their index positions and named capture groups. Use for validating user inputs, extracting structured data from text, or debugging regex patterns. Supports flags g, i, m, s, u, y.Connector