"Understanding Structured Thinking" matching MCP connectors:
GET /v1/connectors — MCP directory API referenceMatching Connector Tools:
Memwyre is an MCP-native persistent memory layer for AI agents, synchronizing context across Claude Code, Cursor, VS Code, and OpenClaw. Built with a high-precision retrieval architecture (dense vector search, BM25, and cross-encoder reranking), Memwyre achieves a benchmarked 73.1% accuracy on the Long-Context Memory (LoCoMo) benchmark. It provides secure, isolated knowledge vaults with dedicated tools (search_memwyre, save_memory, list_memories) to save and recall structured project decisions.
Give AI assistants secure access to your organization's structured business data. Search records, create and update records, retrieve schema information, and manage workflow states using natural language. You need two values for every request: x-api-key — your Web Data Forms API Key x-group-id — your Web Data Forms Group ID You can find these in your Web Data Forms accounts group->information page. Preferred method: request header When possible, pass the credentials as HTTP headers: x-api-key: <your-api-key> x-group-id: <your-group-id> This is the preferred option because it keeps credentials out of the URL and is more secure. Fallback method: query parameters If your MCP client does not support custom headers, the server also accepts the credentials as URL query parameters. Example: https://mcp.webdataforms.com?x-api-key=abc123&x-group-id=xyz456 Detailed information here: https://github.com/Web-Data-Forms/mcp-server-docs/blob/main/README.md
**ColdState Knowledge Search MCP Server** https://github.com/daniel-coldstate/coldstate-mcp Semantic search over 64.6M knowledge entries — the structured alternative to web search APIs and web scraping for LLM agents. No crawling, no rate limits, sub-3s responses. Cloud-hosted at services.coldstate.ai
Shared semantic graph for AI reviews, classification and structured memory across AI assistants.
TestGraph is a shared structured knowledge and review graph for AI agents. Its MCP server lets ChatGPT, Claude and other AI clients store, retrieve and collaboratively refine reviews, entities, relationships and semantic classifications, providing persistent knowledge that can be reused across models and conversations.
Human-authored personal context before AI guidance, built through private structured reflection.
Urban intelligence knowledge graph. Structured locality data for civic problem-solving.
Agent Module provides structured, validated knowledge bases engineered for autonomous agent consumption at runtime. Agents retrieve deterministic knowledge instead of scanning unstructured web content — eliminating hallucinated citations in regulated domains.
Real engineering memory marketplace for autonomous AI agents. Query structured lessons (problem → root cause → solution + confidence 80-100%) extracted from real GitHub/Slack incidents. Categories: debugging, architecture, performance, security, infrastructure & more.
- DocsieOAuth via extensionio.docsie.app
Docsie MCP brings your documentation workflow directly into your AI assistant. Create, edit, and publish documentation without switching between tools. Convert videos into structured documentation instantly, run compliance analysis across text, audio, and video content, and use semantic search to find exactly what you need across your knowledge base. Whether you're building product documentation, SOPs, user guides, or technical manuals, Docsie MCP makes documentation faster, smarter, and powered
RChilli MCP Hub is a production-grade MCP server that exposes RChilli's full HR data intelligence platform as 17 AI-callable tools across 4 categories. Built on 15+ years of HR data intelligence, it is trusted by ATS vendors, HR technology platforms, staffing agencies, and enterprise recruiting teams worldwide. Every tool is read-only and returns a consistent, structured JSON response — no raw exceptions, no inconsistent formats.
Full-control memory API for AI agents with direct access to structured data, relationships, and revision history. Built for advanced workflows, it enables precise querying, linking, and maintenance of a complete memory graph with full transparency and control.
Parse dreams into structured context and search DreamGraph concepts without storing dream text.
Turn documents into structured, AI-ready data by parsing, enriching, chunking, and embedding.
Grimoire turns your TTRPG campaign into a structured, queryable database and knowledge graph and exposes it to any MCP-compatible AI client. Your campaign becomes first-class context for whatever client you bring.
Structured failure knowledge for AI agents — dead ends, workarounds, error chains
A portable context layer for MCP-speaking AI clients. Connect any client to one endpoint and it boots with your containers — structured context, live work state, and accumulated knowledge — carried across every client you use.
- PenlogOAuthapp.penlog.mcp
Penlog is an Apple Pencil journaling app for iPad that reads each handwritten page into structured tasks, notes, and priorities. Its MCP server exposes that journal to your AI: search pages by topic, pull open tasks with reconciled status, read a full day's page, and create or update tasks that appear on your handwritten page and sync to Notion. Write by hand. Work with everything.
LLMtoMD is the memory layer for AI coding agents. It converts any document — PDF, DOCX, slides, spreadsheets, images, audio, even whole websites — into clean, structured Markdown, then exposes it over MCP so your agent can search your FRDs, specs, and API docs on demand instead of re-reading (or forgetting) them.
Structured knowledge base for AI agent solutions. Search, explore, and retrieve build logs.
