MCP RAG
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP RAGWhat are the supported document formats?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP RAG 工具集
基于模型上下文协议(MCP)的智能知识库系统,提供文档处理、知识问答和向量库管理功能。
支持使用豆包与OpenAI
✨ 主要特性
🧠 智能知识库:基于向量检索的 RAG 系统,支持语义搜索和智能问答
📄 多格式文档处理:支持超过 25 种文档格式,包括 PDF、DOCX、PPTX、XLSX、图片、邮件等
🌐 直观 Web 界面:Bento 风格布局,分类展示所有工具功能
🤖 多模型支持:兼容 OpenAI、豆包、Ollama 等主流 AI 模型
🔍 高级过滤搜索:支持按文件类型、内容结构等条件进行精确检索
📊 统计分析:提供知识库统计、嵌入缓存分析等数据洞察
⚡ 本地化处理:支持本地模型推理,保护数据隐私
🔧 向量库管理:提供缓存清理、数据库优化等维护功能
Related MCP server: Chalee MCP RAG
安装
# 安装工具
uv tool install mcp_rag
# 升级工具
uv tool install mcp_rag --upgrade
# 卸载工具
uv tool uninstall mcp_rag使用
启动 MCP 服务器
mcp_rag server启动 Web 界面
mcp_rag webWeb 界面提供直观的 Bento 布局,支持以下工具分类:
📥 添加内容:添加文本和文档到知识库
❓ 智能问答:基于知识库进行问答和检索
📊 数据统计:查看知识库和系统统计信息
⚙️ 向量库管理:优化和维护向量数据库
配置
在项目根目录创建 .env 文件进行配置:
# OpenAI 配置
OPENAI_API_KEY=
OPENAI_API_BASE=https://api.openai.com/v1
OPENAI_MODEL=gpt-4o-mini
OPENAI_TEMPERATURE=0
OPENAI_EMBEDDING_MODEL=text-embedding-3-large
# 豆包 配置
# OPENAI_API_KEY=
# OPENAI_API_BASE=https://ark.cn-beijing.volces.com/api/v3
# OPENAI_MODEL=doubao-1-5-pro-32k-250115
# OPENAI_TEMPERATURE=0
# OPENAI_EMBEDDING_MODEL=doubao-embedding-text-240715mcp客户端配置(豆包为例)
{
"mcpServers": {
"rag": {
"command": "uv",
"args": [
"run",
"mcp-rag",
"serve"
],
"env": {
"PYTHONUNBUFFERED": "1",
"OPENAI_API_KEY": "key",
"OPENAI_API_BASE": "https://ark.cn-beijing.volces.com/api/v3",
"OPENAI_MODEL": "doubao-1-5-pro-32k-250115",
"OPENAI_TEMPERATURE": "0",
"OPENAI_EMBEDDING_MODEL": "doubao-embedding-text-240715",
}
}
}
}可用工具
添加内容
learn_text(text, source_name)- 添加文本到知识库learn_document(file_path)- 处理并添加文档到知识库
智能问答
ask_rag(query)- 基于知识库回答问题ask_rag_filtered(query, file_type, min_tables, min_titles, processing_method)- 带过滤条件的智能检索
支持格式
支持超过 25 种文档格式,包括 PDF、DOCX、PPTX、XLSX、图片、邮件等。
Available Tools
1 toolask_ragB
用户想查询已有资料或者需要知识库时调用
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states a trigger condition, not the tool's actual behavior (e.g., retrieving relevant documents and generating an answer), potential side effects, or authentication requirements. This is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that efficiently communicates the primary use case without any wasted words. It is appropriately brief for a simple tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema (which eliminates the need to explain return values), the description is too sparse to provide complete context. It does not explain what the tool does with the query, how it retrieves information, or any constraints. For a 1-parameter tool, this is still insufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate for explaining the 'query' parameter. It does not describe what the query should contain, its format, or how it is used. The parameter name is self-explanatory, but no additional semantics are added beyond the schema's type and title.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as a query mechanism for existing materials or a knowledge base, using the verb '查询' (query) and specifying the resource. It is distinct enough given the context, though it could be more explicit about the nature of the retrieval (e.g., RAG-based response).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear when-to-use condition: 'when the user wants to query existing materials or needs a knowledge base.' It implies a specific context and is actionable, though it lacks explicit when-not-to-use guidance or alternative tools since no siblings are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
TDQS
Only one tool exists, so there is no risk of ambiguity between tools. The tool's purpose is clear enough for an agent to identify when to use it.
The single tool name 'ask_rag' follows a clear verb_noun convention, which is consistent even with only one tool.
A RAG server typically requires tools for managing the knowledge base (add, delete, list) in addition to querying. Having only one tool is too few for the apparent scope of a RAG server.
The server only supports querying an existing knowledge base, with no way to ingest, update, or manage documents. This leaves significant gaps in the RAG workflow.
Maintenance
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