Knowledge MCP
A high-precision local knowledge base server enabling AI agents to navigate, search, and reason about complex codebases using hybrid semantic, lexical, and graph retrieval.
README
Knowledge MCP
A high-precision local knowledge base server (RAG) that implements the Model Context Protocol (MCP). It enables AI agents (Codex, Claude Code, Gemini CLI, etc.) to navigate, search, and reason about complex codebases using a hybrid approach combining semantic, lexical, and structural analysis.
🚀 Key Features
- Hybrid Triple Search: Fuses three distinct retrieval channels through Reciprocal Rank Fusion (RRF) for maximum recall:
- Full-Text Search (FTS5): Handles exact name matches and specific keywords.
- Semantic Vector Search (
sqlite-vec): Understands concepts and natural language intent. - Graph-Based Retrieval: Provides a 2x relevance boost to actual code symbols and their relationships.
- Deep Semantic Indexing:
- C# / .NET: Integrated Roslyn analysis for precise symbol extraction and dependency graphs.
- Polyglot Support: Tree-sitter integration for high-quality parsing of TS, JS, Python, Go, and more.
- Markdown: Section-aware chunking for documentation.
- Knowledge Graph: Tracks relationships between symbols:
CALLS,INHERITS,IMPLEMENTS, andIMPORTS. Supports recursive Impact Analysis to estimate the blast radius of code changes. - Autonomous Embeddings: In-process generation using
sentence-transformers(mpnet-base-v2). Works natively inside Docker without external API dependencies. - Incremental Sync: Delta-sync mechanism via
mtimeandSHA-256ensures only modified files are processed, significantly speeding up updates.
🛠 Available MCP Tools
Search & Retrieval
knowledge_search: Triple hybrid search across all repositories. Returns chunks with trust levels (verifiedfor code,hintfor docs).knowledge_get_chunk: Retrieve detailed content and metadata for a specific knowledge chunk.
Symbol & Graph Navigation
knowledge_find_symbol: Locate classes, methods, and interfaces using wildcards (e.g.,*Repository).knowledge_get_callers/knowledge_get_callees: Navigate the call graph of any symbol.knowledge_get_hierarchy: Explore inheritance and interface implementations.knowledge_impact_analysis: Perform recursive dependency analysis to find everything affected by a symbol change.
Management
knowledge_sync_repo: Trigger a background delta-sync for a repository to update the AI's "memory" after code changes.knowledge_delete_repo: Wipe all indexed data for a specific repository.
📋 Requirements
- Python 3.10+
- .NET 8.0 SDK (Required for Roslyn-based C# analysis)
- Docker (Recommended for easiest deployment)
🐳 Quick Start (Docker)
1. Configure your repository path
Create a .env file in the project root:
# The host directory that will be mounted as /repos inside the container.
# Set this to the PARENT folder of your repositories.
REPOS_DIR=C:\Repos
2. Build and start the container
docker compose up -d
3. Index a specific project
Option A — PowerShell script (recommended, see Scripts):
.\scripts\Reindex-Repo.ps1 -Wait
Option B — direct HTTP call:
curl -X POST http://localhost:8000/sync \
-H "Content-Type: application/json" \
-d '{"repo_id": "my-app", "repo_path": "/repos/my-app"}'
⚠️
repo_pathis the path inside the container (e.g.,/repos/my-app), not the host path.
4. Connect your MCP Client
Add this to your AI client's config (e.g., mcp.json):
{
"mcpServers": {
"knowledge-mcp": {
"command": "docker",
"args": ["exec", "-i", "knowledge-mcp", "python", "-m", "knowledge_mcp.main", "mcp"]
}
}
}
🔧 Scripts
scripts/Reindex-Repo.ps1
A PowerShell helper script that triggers re-indexing of a repository by sending a POST request to the running knowledge-mcp server.
Parameters:
| Parameter | Default | Description |
|---|---|---|
-RepoId |
ImpactOS.Core.Lib |
Unique repository identifier used as a key in the database |
-RepoPath |
/repos/ImpactOS.Core.Lib |
Path to the repository inside the Docker container |
-ServerUrl |
http://localhost:8000 |
URL of the running knowledge-mcp server |
-Wait |
$false |
If set, streams container logs after triggering sync |
Usage examples:
# Trigger re-indexing with default settings (ImpactOS.Core.Lib)
.\scripts\Reindex-Repo.ps1
# Trigger and watch progress in real time
.\scripts\Reindex-Repo.ps1 -Wait
# Index a different repository
.\scripts\Reindex-Repo.ps1 -RepoId "MyOtherLib" -RepoPath "/repos/MyOtherLib"
# Point to a remote server
.\scripts\Reindex-Repo.ps1 -ServerUrl "http://192.168.1.100:8000" -Wait
How it works:
- Verifies the server is reachable at
ServerUrl. - Sends a
POST /syncrequest withrepo_idandrepo_path. - Indexing runs in the background inside the container.
- With
-Wait, streams live logs viadocker logs -f.
💡 Path mapping: If
REPOS_DIR=C:\Repos, thenC:\Repos\MyLibon the host is accessible inside the container as/repos/MyLib.
📐 Architecture & Decisions
For deep dives into the technical design, see our Architecture Decision Records:
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。