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162,699 tools. Last updated 2026-05-30 10:18
"Thinking process for developing or using Cursor AI" matching MCP tools:
- Interleaved cross-org release feed for a collection — same shape as `get_latest_releases` but scoped to the collection's member orgs. Cursor-paginated: pass `limit` for slice size (default 20), `cursor` to continue from a prior call. The result's `_meta.pagination` carries `kind: 'cursor'`, `hasMore`, and `nextCursor` when more rows exist; the response text echoes `nextCursor` so an LLM caller can chain without parsing `_meta`. Cursors are stable under inserts.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
- Discover AXIS install metadata, pricing, and shareable manifests for commerce-capable agents. Free, no auth, and no mutation beyond read access. Example: call before wiring AXIS into Claude Desktop, Cursor, or VS Code. Use this when you need onboarding and ecosystem setup details. Use search_and_discover_tools instead for keyword routing or discover_agentic_purchasing_needs for purchasing-task triage.Connector
- WHEN: mapping the technical D365 objects behind a business process, or understanding which tables/forms implement a flow. Triggers: 'processus métier', 'Order-to-Cash', 'Procure-to-Pay', 'Record-to-Report', 'business process flow', 'qui est impliqué dans', 'map the process', 'flux du processus', 'quels objets dans le flux'. Map a D365 F&O business process to its complete object chain. For known processes (Order-to-Cash, Procure-to-Pay, Record-to-Report, Plan-to-Produce, Inventory-Management, Hire-to-Retire, Project-Accounting, Asset-Lifecycle): shows every step with forms, tables, classes, entities, reports, and security roles involved. For any other object name: traces all dependencies (tables, classes, forms, entities) from that entry point. Produces a Mermaid process flow diagram. Use 'list' to see all known process mappings. NOT for a single object's FK relations only -- use `find_related_objects` for that (faster and more precise).Connector
- Compute text similarity using local algorithms (Bag of Words, TF-IDF, Character N-grams). No API key needed — runs entirely in-process. NOT real embeddings: for true semantic similarity with vector embeddings, use run_semantic_tests with mode="embeddings" and your OpenAI API key. Supports single pair or batch mode with pipe-separated pairs. Useful for RAG retrieval testing, semantic search evaluation, and text deduplication.Connector
Matching MCP Servers
- Alicense-qualityBmaintenanceEnables AI agents to query OpenRouter model information including prices, ELO rankings, context, and perform comparisons.Last updated3,3361MIT
- Alicense-qualityCmaintenanceProvides 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 updated64Apache 2.0
Matching MCP Connectors
Find relevant Smart‑Thinking memories fast. Fetch full entries by ID to get complete context. Spee…
The Process Street MCP Server enables AI agents to query workflows, complete tasks, trigger runs, update form fields, search records, and pull structured operational data with full auditability. Built for compliance-first teams in financial services, healthcare, government, and enterprise operations.
- Cursor-paginated browse over the catalog. Quality-first: by default excludes questions flagged for review (use quality='all' for full pool). USE WHEN: full catalog sync, delta sync (updated_since), exhaustive enumeration by filter. NOT WHEN: you only need N random samples (use quizbase_random) or a single record (use quizbase_question_by_id). PAGINATION: stable cursor over id UUIDv7 DESC. First call: omit cursor. Next: pass meta.nextCursor. Stop when nextCursor is null. KEY FILTERS (full parity with REST): - lang: ISO 639-1, default "en". Supported: en, pl. - category (slug), difficulty (trivial|easy|medium|hard|expert — LLM-calibrated), type (multiple|boolean), subcategory (raw slug). - tags (AND), tags_any (OR, max 10): raw tag slugs. - topic (curated, alias resolver), topics_any (OR over curated): higher precision than tags. - regions (cultural affinity, AND): empty = no cultural advantage assumed. Lowercase ISO 3166-1 alpha-2 ('us', 'pl', 'gb') + cultural codes ('jewish', 'christian-catholic', 'islam'). Filter for content statistically more likely known by residents/members. Discover via quizbase_regions. - source: one of 12 (opentdb, opentriviaqa, kqa-pro, entityq, mintaka, mkqa, nq-open, creak, qasc, arc, webq, quizbase). - license (SPDX): e.g. CC-BY-SA-4.0, MIT. - quality: 'high' (default) excludes questions flagged for review; 'all' for full approved pool. When 'all', each question gains a "quality" field with value 'high' or 'needs_review'. - updated_since (ISO 8601): only questions updated after this — for delta sync caches. PAGINATION + COUNTING: - cursor (string): from previous meta.nextCursor. Omit for page 1. - limit (1-100, default 20). - count: estimate (default, EXPLAIN-based ~5-20ms, ±5-50%) | none (skip). OUTPUT: { questions: [...], meta: { count, countMode, language, nextCursor, totalEstimate? } }. Each question carries full per-record attribution (source, author, license, licenseVersion, licenseUrl, sourceId, url, modifications, lastModified) — identical shape to REST /api/v1/questions. ATTRIBUTION REQUIRED if you redistribute. Credit each question using its own attribution object — see license + licenseUrl + modifications fields per record. COMMON MISTAKES: not passing the cursor on subsequent calls (you'll re-read page 1); polling without updated_since when doing delta sync.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
- [tourradar] Search tour reviews using AI-powered semantic search. Requires tourIds to scope results to specific tours. Use this when the user asks about reviews, feedback, or experiences for specific tours. Combine with an optional text query to find reviews mentioning specific topics (e.g., 'food', 'guide', 'accommodation'). When you don't have tour IDs, use vertex-tour-search or vertex-tour-title-search first to find them.Connector
- Test a message against an AI filter to check whether it would match. This tool embeds the provided message using Voyage AI and computes the cosine similarity between the message vector and the filter's stored reference vector. It returns the similarity score, whether the message would match (similarity >= threshold), and the filter's threshold value. Use this to: - Verify a filter works as intended before using it in a trigger - Tune the threshold by testing borderline messages - Debug why a message did or did not match a filter in production Returns: {similarity: float, matched: bool, threshold: float} Note: This tool calls the Voyage AI embedding API to embed the test message.Connector
- List available AI models grouped by thinking level (low/medium/high). Shows default models, credit costs, capabilities for each tier. Use this before consult to understand model options.Connector
- Quick AI visibility scan. Returns three scores: AEO Score (0-100, AI search engine findability), GEO Score (0-100, AI citation readiness), and Agent Readiness Score (0-100, AI agent interaction capability). Also returns AI Identity Card with mention readiness (0-100, predicts how likely AI will mention the brand), detected competitors, business profile (commerce/saas/media/general), and top 5 issues. 67+ checks across 12 categories. Free — no API key needed. Does NOT return per-check details or fix code — use audit_site for full breakdown, fix_site for generated fixes, compare_sites to benchmark against a competitor.Connector
- List available AI models grouped by thinking level (low/medium/high). Shows default models, credit costs, capabilities for each tier. Use this before consult to understand model options.Connector
- MCP.AI for IDE agents (Cursor, etc.): log in in the browser, copy the access token, paste here. Call with { token: "<jwt>" } after the user pastes, or with no args to get the link.Connector
- Read-only. Use to query Dreamlit analytics for overview metrics, notification rows, recipient engagement, or workflow run rows with filters, sorting, and cursor pagination. Returns bounded structured analytics data, effective query metadata, pagination details when rows are included, and relevant app URLs. Do not use for CSV exports, bulk dumps, workflow edits, publishing, or low-level database access.Connector
- Find alternatives to a brand using the knowledge graph, shared capabilities, and category matching. Each alternative includes WHY it's an alternative. Args: slug: The brand slug (e.g. "cursor", "salesforce"). limit: Max alternatives (default 10, max 20). Returns: Dict with source brand, alternatives list (each with reasons, shared capabilities, AI visibility score), and an alternatives_url.Connector
- Get Lenny Zeltser's fill-in-the-blank template for planning a security product strategy. Includes strategic questions organized by section with evidence columns. This server never requests your product plans and instructs your AI to keep them local—guidelines flow to your AI for local analysis. The template is Copyright (c) 2026 Lenny Zeltser; any content you create using it is entirely yours.Connector
- Get the AI Defense Matrix cross-mapping playbook for mapping product capabilities to matrix cells: coverage taxonomy (primary, secondary, partial, aspirational), differentiation guidance, disambiguation block, worked examples, and out-of-scope examples. The response always includes an inScopeCheck. Products that USE AI to solve a non-AI security problem (deepfake detection, AI-for-fraud, AI features added to existing SIEM, SOAR, or EDR tools) belong in the Cyber Defense Matrix at https://cyberdefensematrix.com. Pairs naturally with product_load_context(productFocus: 'ai_security') for follow-on positioning and GTM work. 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
- Start asynchronous AI research for a contact using LinkedIn and other sources, then poll the research ID for results.Connector
- Get a 24-hour AI-generated summary for any crypto ticker or topic (paid via x402). Returns decision-grade bullet points combining Gloria's curated news with real-time web search. Designed for fund managers and trading agents. Payment is handled via the x402 protocol using USDC on Base network. This tool returns the payment endpoint and instructions.Connector