"A local knowledge base" matching MCP servers:

Enables clients to access 21 cloud-hosted financial tools via a token-authenticated forwarding layer that relays tool calls to the Silkj E-Investment skill gateway. Supports stdio, Streamable HTTP, and hosted deployment modes without embedding any financial algorithms locally.

Exposes a knowledge base to LLM clients through tools, resources, and prompts.

Enables automated labor and service contract risk review through MCP, supporting single or batch contract analysis, risk knowledge base queries, and regional regulation lookups.

Enables AI assistants to search, retrieve, and browse Markdown documentation in a local folder, then answer questions with cited sources.

Enables Claude to save, retrieve, update, search, and organize personal ideas and reusable analytical frameworks in a private SQLite-backed knowledge base. Supports local desktop use or always-on access across a user's own devices over a private network.

A knowledge base MCP server that aggregates team knowledge from multiple sources into Postgres. It provides hybrid search (full-text + vector + RRF) via MCP tools, and enables direct recording of decisions, learnings, and pitfalls.

提供全球238个国家/地区的进出口贸易数据查询，支持按企业名称、产品关键字、HS编码等多维度联合搜索。

A custom MCP protocol service that enhances AI models by providing multilingual translation capabilities and resource management, allowing for automatic text extraction and translation through external APIs.

Enables AI agents to persist, search, and evolve knowledge through a Markdown vault with a typed knowledge graph and MCP interface.

Enables AI agents to share a persistent, self-improving memory by ingesting, classifying, synthesizing, and retrieving knowledge from sources like Obsidian vaults, code, and transcripts through MCP and REST APIs. Provides token-optimized, tiered retrieval with full-text and semantic search so every agent session builds on accumulated context.

Enables AI agents to capture, classify, synthesize, and retrieve persistent shared knowledge from sources such as Obsidian, code, transcripts, bookmarks, and sessions. It provides token-efficient search, context briefings, and tiered memory through MCP and REST APIs.

Enables AI agents to share a persistent, self-learning memory by ingesting and classifying knowledge from Obsidian, code, transcripts, bookmarks, and terminal sessions, then retrieving token-optimized context through MCP and REST APIs.

A cross-project knowledge base MCP server supporting three-layer search (text matching, TF-IDF, and semantic vectors) with a Web UI for management.

Enables AI agents to share persistent memory by capturing, classifying, synthesizing, and retrieving knowledge through token-optimized, three-tier retrieval with hybrid keyword and semantic search. Supports bidirectional Obsidian sync, session and bug-fix capture, and a self-learning loop that compounds context across sessions.

Enables AI agents and clients to capture, classify, synthesize, and retrieve persistent knowledge from multiple sources through MCP and REST APIs, with tiered memory and token-optimized context.

Gives AI agents a shared persistent memory by ingesting Obsidian vaults, code, transcripts, web clips, and session or bug-fix captures, then serving token-optimized context briefings, keyword and semantic search, and cross-source synthesis. Enables multiple agents to read and write the same growing knowledge base so each session builds on the last.

Enables AI agents to share persistent memory by ingesting, classifying, synthesizing, and retrieving knowledge through MCP and REST APIs, with token-optimized, tier-aware search and Obsidian sync.

Enables AI agents to share a persistent, self-improving memory by ingesting sources like Obsidian vaults, code, transcripts, and session captures, then serving them back through search, classification, synthesis, and token-optimized context briefings. It lets multiple agents read and write the same knowledge base, so each session builds on the last without copy-pasting context.

Enables coding agents to query the dependency graph of a GitHub backlog, retrieve ordered ready work and prerequisite chains, atomically claim issues, and report blockers—so agents work in the correct order and safely in parallel.

Enables AI agents to share persistent memory by ingesting sources, classifying and synthesizing knowledge, and retrieving token-optimized context through MCP and REST APIs.