基于 MCP 构建智能文档分析系统：技术实现详解

亲爱的非洲野猪2026-01-16 9:49

在信息爆炸的时代，企业每天处理数以千计的文档------从技术手册、合同协议到研究报告。传统的关键词搜索已无法满足深度分析需求。本文将详细讲解如何基于 MCP（Model Context Protocol） 构建一个能理解、分析并智能推荐关联信息的文档分析系统。

一、系统架构设计

1.1 整体架构

复制代码 
┌─────────────────────────────────────────────────┐
│                AI 应用层 (Claude/ChatGPT)        │
├─────────────────────────────────────────────────┤
│           MCP 协议层 (安全工具调用)                │
├─────────────────────────────────────────────────┤
│  ┌───────┐  ┌───────┐  ┌───────┐  ┌───────┐     │
│  │文档提取│  │向量存储│   │知识图谱│  │关联推荐│     │
│  │ MCP   │  │ MCP   │  │ MCP   │  │ MCP   │     │
│  └───────┘  └───────┘  └───────┘  └───────┘     │
├─────────────────────────────────────────────────┤
│      文档存储 │  向量数据库   │   图数据库          │
└─────────────────────────────────────────────────┘

1.2 技术栈选择

  • MCP 框架 : @modelcontextprotocol/sdk (Node.js/Python)
  • 文档处理: Unstructured.io, PDF.js, mammoth (Word)
  • 向量计算: Sentence Transformers, OpenAI Embeddings
  • 向量存储: ChromaDB, Pinecone, Qdrant
  • 图数据库: Neo4j, NebulaGraph
  • 缓存层: Redis
  • 任务队列: Bull (Node.js) 或 Celery (Python)

二、MCP 服务器详细实现

2.1 文档提取服务器 (document-extractor-server)

typescript 复制代码 
// document-extractor-server.ts
import { Server } from '@modelcontextprotocol/sdk/server/index.js';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
import { extractText, extractMetadata, chunkDocument } from './processors/index.js';
import { createHash } from 'crypto';

export class DocumentExtractorServer extends Server {
  private processedCache = new Map<string, any>();
  
  constructor() {
    super({
      name: 'document-extractor',
      version: '1.0.0'
    });
    
    this.setupTools();
  }
  
  private setupTools() {
    // 工具1: 提取文档内容
    this.setRequestHandler('tools/list', async () => ({
      tools: [
        {
          name: 'extract_document',
          description: '从各种格式文档中提取结构化内容',
          inputSchema: {
            type: 'object',
            properties: {
              filepath: { type: 'string' },
              chunk_size: { type: 'number', default: 1000 },
              overlap: { type: 'number', default: 200 }
            },
            required: ['filepath']
          }
        },
        {
          name: 'extract_from_url',
          description: '从URL提取网页内容',
          inputSchema: {
            type: 'object',
            properties: {
              url: { type: 'string' },
              selector: { type: 'string' }
            },
            required: ['url']
          }
        }
      ]
    }));
    
    // 工具执行处理器
    this.setRequestHandler('tools/call', async (request) => {
      switch (request.params.name) {
        case 'extract_document':
          return await this.handleExtractDocument(request.params.arguments);
        case 'extract_from_url':
          return await this.handleExtractFromUrl(request.params.arguments);
        default:
          throw new Error(`Unknown tool: ${request.params.name}`);
      }
    });
  }
  
  private async handleExtractDocument(args: any) {
    const { filepath, chunk_size, overlap } = args;
    
    // 检查缓存
    const hash = createHash('md5').update(filepath).digest('hex');
    if (this.processedCache.has(hash)) {
      return this.processedCache.get(hash);
    }
    
    // 提取文档
    const content = await extractText(filepath);
    const metadata = await extractMetadata(filepath);
    const chunks = await chunkDocument(content, chunk_size, overlap);
    
    const result = {
      metadata,
      chunks,
      total_chunks: chunks.length,
      file_size: content.length
    };
    
    // 缓存结果 (5分钟)
    this.processedCache.set(hash, result);
    setTimeout(() => this.processedCache.delete(hash), 300000);
    
    return {
      content: [{
        type: 'text',
        text: JSON.stringify(result, null, 2)
      }]
    };
  }
}

// 启动服务器
const server = new DocumentExtractorServer();
const transport = new StdioServerTransport();
await server.connect(transport);

2.2 向量嵌入服务器 (embedding-server)

python 复制代码 
# embedding_server.py
from mcp.server import Server, stdio_server
from sentence_transformers import SentenceTransformer
import numpy as np
import asyncio
from typing import List, Dict
import pickle
import lzma

class EmbeddingServer(Server):
    def __init__(self):
        super().__init__("embedding-server", "1.0.0")
        # 加载多语言模型
        self.models = {
            "all-MiniLM-L6-v2": SentenceTransformer('all-MiniLM-L6-v2'),
            "paraphrase-multilingual-MiniLM-L12-v2": 
                SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')
        }
        self.cache = {}
        
    async def list_tools(self):
        return {
            "tools": [
                {
                    "name": "generate_embeddings",
                    "description": "为文本生成向量嵌入",
                    "inputSchema": {
                        "type": "object",
                        "properties": {
                            "texts": {
                                "type": "array",
                                "items": {"type": "string"},
                                "description": "要嵌入的文本列表"
                            },
                            "model_name": {
                                "type": "string",
                                "enum": list(self.models.keys()),
                                "default": "all-MiniLM-L6-v2"
                            },
                            "normalize": {"type": "boolean", "default": True}
                        },
                        "required": ["texts"]
                    }
                },
                {
                    "name": "semantic_search",
                    "description": "语义搜索相似内容",
                    "inputSchema": {
                        "type": "object",
                        "properties": {
                            "query": {"type": "string"},
                            "embeddings": {"type": "array", "items": {"type": "array"}},
                            "k": {"type": "number", "default": 5}
                        },
                        "required": ["query", "embeddings"]
                    }
                }
            ]
        }
    
    async def handle_generate_embeddings(self, texts: List[str], 
                                         model_name: str = "all-MiniLM-L6-v2",
                                         normalize: bool = True) -> Dict:
        cache_key = hash(tuple(texts) + (model_name, normalize))
        if cache_key in self.cache:
            return self.cache[cache_key]
        
        model = self.models[model_name]
        embeddings = model.encode(texts, normalize_embeddings=normalize)
        
        # 压缩存储
        compressed = lzma.compress(pickle.dumps(embeddings.tolist()))
        
        result = {
            "embeddings": embeddings.tolist(),
            "dimension": embeddings.shape[1],
            "compressed_size": len(compressed),
            "model": model_name
        }
        
        self.cache[cache_key] = result
        return result
    
    async def handle_semantic_search(self, query: str, embeddings: List, k: int = 5):
        query_embedding = self.models["all-MiniLM-L6-v2"].encode([query])[0]
        embeddings_array = np.array(embeddings)
        
        # 计算余弦相似度
        similarities = np.dot(embeddings_array, query_embedding) / (
            np.linalg.norm(embeddings_array, axis=1) * np.linalg.norm(query_embedding)
        )
        
        top_k_indices = np.argsort(similarities)[-k:][::-1]
        
        return {
            "indices": top_k_indices.tolist(),
            "scores": similarities[top_k_indices].tolist()
        }

async def main():
    server = EmbeddingServer()
    await server.run(stdio_server())

if __name__ == "__main__":
    asyncio.run(main())

2.3 知识图谱构建服务器 (knowledge-graph-server)

javascript 复制代码 
// knowledge-graph-server.js
import { Server } from '@modelcontextprotocol/sdk/server/index.js';
import neo4j from 'neo4j-driver';
import { OpenAI } from 'openai';

export class KnowledgeGraphServer extends Server {
  constructor() {
    super({ name: 'knowledge-graph', version: '1.0.0' });
    
    // 连接 Neo4j
    this.driver = neo4j.driver(
      process.env.NEO4J_URI,
      neo4j.auth.basic(process.env.NEO4J_USER, process.env.NEO4J_PASSWORD)
    );
    
    this.openai = new OpenAI({
      apiKey: process.env.OPENAI_API_KEY
    });
    
    this.setupTools();
  }
  
  setupTools() {
    this.setRequestHandler('tools/list', async () => ({
      tools: [
        {
          name: 'extract_entities',
          description: '从文本中提取实体和关系',
          inputSchema: {
            type: 'object',
            properties: {
              text: { type: 'string' },
              document_id: { type: 'string' }
            },
            required: ['text']
          }
        },
        {
          name: 'query_related',
          description: '查询相关实体和路径',
          inputSchema: {
            type: 'object',
            properties: {
              entity: { type: 'string' },
              relation_type: { type: 'string' },
              depth: { type: 'number', default: 2 }
            },
            required: ['entity']
          }
        },
        {
          name: 'find_similar_documents',
          description: '基于知识图谱查找相似文档',
          inputSchema: {
            type: 'object',
            properties: {
              document_id: { type: 'string' },
              limit: { type: 'number', default: 5 }
            },
            required: ['document_id']
          }
        }
      ]
    }));
  }
  
  async handleExtractEntities(args) {
    const { text, document_id } = args;
    
    // 使用 OpenAI 提取实体和关系
    const completion = await this.openai.chat.completions.create({
      model: "gpt-4",
      messages: [{
        role: "system",
        content: `提取文本中的实体和关系，返回JSON格式：
        {
          "entities": [{"name": "", "type": "PERSON|ORG|CONCEPT|LOCATION"}],
          "relations": [{"source": "", "target": "", "type": "WORKS_FOR|MENTIONS|RELATED_TO"}]
        }`
      }, {
        role: "user",
        content: text.substring(0, 4000)
      }],
      response_format: { type: "json_object" }
    });
    
    const result = JSON.parse(completion.choices[0].message.content);
    
    // 存储到 Neo4j
    const session = this.driver.session();
    try {
      // 创建文档节点
      await session.run(
        `MERGE (d:Document {id: $docId})
         SET d.processed_at = datetime()`,
        { docId: document_id }
      );
      
      // 创建实体节点和关系
      for (const entity of result.entities) {
        await session.run(
          `MERGE (e:Entity {name: $name, type: $type})
           MERGE (d:Document {id: $docId})
           MERGE (e)-[:APPEARS_IN]->(d)`,
          { name: entity.name, type: entity.type, docId: document_id }
        );
      }
      
      // 创建实体间关系
      for (const rel of result.relations) {
        await session.run(
          `MATCH (a:Entity {name: $source})
           MATCH (b:Entity {name: $target})
           MERGE (a)-[r:${rel.type}]->(b)
           SET r.source_document = $docId,
               r.extracted_at = datetime()`,
          { source: rel.source, target: rel.target, docId: document_id }
        );
      }
      
    } finally {
      await session.close();
    }
    
    return {
      content: [{
        type: 'text',
        text: `提取了 ${result.entities.length} 个实体和 ${result.relations.length} 个关系`
      }]
    };
  }
}

2.4 关联推荐服务器 (recommendation-server)

python 复制代码 
# recommendation_server.py
from mcp.server import Server
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from collections import defaultdict
import networkx as nx

class RecommendationServer(Server):
    def __init__(self):
        super().__init__("recommendation-server", "1.0.0")
        self.user_profiles = defaultdict(dict)
        self.document_graph = nx.Graph()
        
    async def list_tools(self):
        return {
            "tools": [
                {
                    "name": "recommend_related",
                    "description": "推荐相关内容",
                    "inputSchema": {
                        "type": "object",
                        "properties": {
                            "content": {"type": "string"},
                            "context": {
                                "type": "object",
                                "properties": {
                                    "recent_docs": {"type": "array", "items": {"type": "string"}},
                                    "user_interests": {"type": "array", "items": {"type": "string"}}
                                }
                            },
                            "max_recommendations": {"type": "number", "default": 10}
                        },
                        "required": ["content"]
                    }
                },
                {
                    "name": "build_user_profile",
                    "description": "基于交互历史构建用户画像",
                    "inputSchema": {
                        "type": "object",
                        "properties": {
                            "user_id": {"type": "string"},
                            "interactions": {
                                "type": "array",
                                "items": {
                                    "type": "object",
                                    "properties": {
                                        "document_id": {"type": "string"},
                                        "action": {"type": "string", "enum": ["view", "save", "share"]},
                                        "duration": {"type": "number"}
                                    }
                                }
                            }
                        },
                        "required": ["user_id", "interactions"]
                    }
                }
            ]
        }
    
    async def handle_recommend_related(self, content, context=None, max_recommendations=10):
        recommendations = []
        
        # 1. 基于内容的协同过滤
        content_embedding = await self.get_embedding(content)
        doc_similarities = self.calculate_similarities(content_embedding)
        
        # 2. 基于知识图谱的推荐
        kg_recommendations = await self.get_kg_recommendations(content)
        
        # 3. 基于用户画像的个性化推荐
        if context and context.get('user_interests'):
            personalized = await self.get_personalized_recommendations(
                content, context['user_interests']
            )
            recommendations.extend(personalized)
        
        # 混合排序算法
        final_recs = self.rank_recommendations(
            doc_similarities,
            kg_recommendations,
            personalized if 'personalized' in locals() else []
        )
        
        return {
            "recommendations": final_recs[:max_recommendations],
            "sources": ["content_based", "knowledge_graph", "collaborative"],
            "reasoning": "基于内容相似度、知识图谱关联和用户兴趣的多维度推荐"
        }
    
    def rank_recommendations(self, *recommendation_lists):
        """混合排序算法"""
        ranked = defaultdict(float)
        
        for i, rec_list in enumerate(recommendation_lists):
            weight = 1.0 / (i + 1)  # 递减权重
            for j, (doc_id, score) in enumerate(rec_list):
                ranked[doc_id] += score * weight * (0.9 ** j)  # 位置衰减
        
        return sorted(ranked.items(), key=lambda x: x[1], reverse=True)

三、系统集成配置

3.1 Claude Desktop 配置

json 复制代码 
// ~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "document-extractor": {
      "command": "node",
      "args": ["./servers/document-extractor-server.js"],
      "env": {
        "NODE_ENV": "production",
        "MAX_FILE_SIZE": "104857600"
      }
    },
    "embedding-server": {
      "command": "python",
      "args": ["./servers/embedding_server.py"],
      "env": {
        "CACHE_DIR": "./cache/embeddings",
        "MODEL_CACHE_SIZE": "10"
      }
    },
    "knowledge-graph": {
      "command": "node",
      "args": ["./servers/knowledge-graph-server.js"],
      "env": {
        "NEO4J_URI": "bolt://localhost:7687",
        "OPENAI_API_KEY": "${OPENAI_API_KEY}"
      }
    },
    "recommendation-engine": {
      "command": "python",
      "args": ["./servers/recommendation_server.py"],
      "env": {
        "REDIS_URL": "redis://localhost:6379"
      }
    },
    "vector-database": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-chromadb", 
               "--persist-dir", "./data/chroma"]
    }
  }
}

3.2 Docker 部署配置

dockerfile 复制代码 
# Dockerfile
FROM node:18-alpine AS base

# 文档提取服务
FROM base AS document-extractor
WORKDIR /app
COPY servers/document-extractor/package*.json ./
RUN npm ci --only=production
COPY servers/document-extractor/ .
CMD ["node", "index.js"]

# 向量服务
FROM python:3.11-slim AS embedding-server
WORKDIR /app
COPY servers/embedding/requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY servers/embedding/ .
CMD ["python", "server.py"]

# docker-compose.yml
version: '3.8'
services:
  document-extractor:
    build:
      context: .
      target: document-extractor
    volumes:
      - ./documents:/documents:ro
      - ./cache:/cache
    environment:
      - CACHE_DIR=/cache
      
  embedding-server:
    build:
      context: .
      target: embedding-server
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    environment:
      - TRANSFORMERS_CACHE=/cache/models
      
  neo4j:
    image: neo4j:5-enterprise
    environment:
      - NEO4J_AUTH=neo4j/password
      - NEO4J_ACCEPT_LICENSE_AGREEMENT=yes
    volumes:
      - neo4j_data:/data
      - neo4j_logs:/logs
      
  redis:
    image: redis:7-alpine
    
  chromadb:
    image: chromadb/chroma:latest
    volumes:
      - chroma_data:/chroma/chroma

四、使用场景示例

4.1 智能文档问答

markdown 复制代码 
用户：请分析这份技术白皮书，并告诉我：
1. 主要技术要点
2. 相关的实施案例
3. 推荐的进一步阅读资料

AI（通过 MCP）：
1. **技术要点分析**：
   - 使用 document-extractor 提取文档内容
   - 通过 embedding-server 识别关键技术概念
   - 使用 knowledge-graph 建立概念关联

2. **实施案例搜索**：
   - 在知识图谱中查询相关案例
   - 语义搜索相似文档
   - 按相关性排序返回

3. **智能推荐**：
   - 基于当前文档向量推荐相似内容
   - 根据知识图谱推荐进阶阅读
   - 结合用户历史推荐个性化内容

4.2 工作流自动化

bash 复制代码 
#!/bin/bash
# 自动化文档处理流水线

# 1. 批量处理文档
curl -X POST http://localhost:8080/mcp/tools/call \
  -H "Content-Type: application/json" \
  -d '{
    "server": "document-extractor",
    "tool": "batch_process",
    "arguments": {
      "directory": "/documents/quarterly",
      "format": ["pdf", "docx"]
    }
  }'

# 2. 构建知识图谱
curl -X POST http://localhost:8080/mcp/tools/call \
  -H "Content-Type: application/json" \
  -d '{
    "server": "knowledge-graph",
    "tool": "build_graph",
    "arguments": {
      "doc_ids": ["doc1", "doc2", "doc3"],
      "relationship_types": ["cites", "mentions", "related_to"]
    }
  }'

# 3. 生成分析报告
curl -X POST http://localhost:8080/mcp/tools/call \
  -H "Content-Type: application/json" \
  -d '{
    "server": "recommendation-engine",
    "tool": "generate_insights",
    "arguments": {
      "time_range": "last_quarter",
      "topics": ["AI", "Security", "Compliance"]
    }
  }'

五、性能优化策略

5.1 缓存策略

javascript 复制代码 
// 多层缓存实现
class OptimizedDocumentServer extends Server {
  constructor() {
    this.cache = {
      memory: new Map(),                    // 内存缓存 (短期)
      redis: new RedisCache(),              // Redis缓存 (中期)
      disk: new DiskCache('./cache'),       // 磁盘缓存 (长期)
      cdn: new CDNProxy()                   // CDN缓存 (静态资源)
    };
    
    // 缓存策略
    this.cacheStrategies = {
      embeddings: { ttl: 3600, level: 'memory' },
      extracted_text: { ttl: 86400, level: 'disk' },
      graph_results: { ttl: 300, level: 'redis' }
    };
  }
  
  async getWithCache(key, fetchFn, strategy) {
    // 多级缓存查询
    for (const level of ['memory', 'redis', 'disk']) {
      const cached = await this.cache[level].get(key);
      if (cached) {
        // 更新更快的缓存层级
        await this.promoteToFasterCache(key, cached, level);
        return cached;
      }
    }
    
    // 缓存未命中，获取数据
    const data = await fetchFn();
    await this.cache[strategy.level].set(key, data, strategy.ttl);
    return data;
  }
}

5.2 异步批处理

python 复制代码 
# batch_processor.py
import asyncio
from concurrent.futures import ThreadPoolExecutor
from typing import List, Callable

class BatchProcessor:
    def __init__(self, max_workers: int = 4, batch_size: int = 10):
        self.executor = ThreadPoolExecutor(max_workers=max_workers)
        self.semaphore = asyncio.Semaphore(max_workers)
        self.batch_size = batch_size
        
    async def process_batch(self, items: List, process_fn: Callable):
        """批量处理文档，优化IO和计算"""
        batches = [items[i:i + self.batch_size] 
                  for i in range(0, len(items), self.batch_size)]
        
        results = []
        for batch in batches:
            async with self.semaphore:
                # 并行处理每个批次
                batch_results = await asyncio.gather(
                    *[self._process_single(item, process_fn) for item in batch],
                    return_exceptions=True
                )
                results.extend(batch_results)
        
        return results
    
    async def _process_single(self, item, process_fn):
        loop = asyncio.get_event_loop()
        # 将CPU密集型任务放到线程池
        return await loop.run_in_executor(self.executor, process_fn, item)

六、安全与权限控制

6.1 细粒度权限管理

yaml 复制代码 
# security-policy.yaml
access_control:
  # 基于角色的访问控制
  roles:
    reader:
      allowed_tools: ["extract_document", "search_documents"]
      allowed_paths: ["/public/*", "/shared/*"]
      
    analyst:
      inherits: ["reader"]
      allowed_tools: ["analyze_trends", "build_graph"]
      allowed_databases: ["analytics_db"]
      
    admin:
      allowed_tools: ["*"]
      allowed_paths: ["/*"]
  
  # 基于属性的访问控制
  attributes:
    - name: "document_sensitivity"
      values: ["public", "internal", "confidential"]
      conditions:
        confidential:
          require: ["security_clearance"]
          ip_range: ["10.0.0.0/8"]
  
  # 审计日志
  audit:
    enabled: true
    log_tool_calls: true
    log_data_access: true
    retention_days: 90

6.2 数据脱敏处理

python 复制代码 
# data_masking.py
import re
from typing import Dict, Any

class DataMaskingMiddleware:
    def __init__(self):
        self.patterns = {
            'email': r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b',
            'phone': r'\b(\+\d{1,3}[-.]?)?\(?\d{3}\)?[-.]?\d{3}[-.]?\d{4}\b',
            'ssn': r'\b\d{3}[-]\d{2}[-]\d{4}\b',
            'credit_card': r'\b\d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4}\b'
        }
        
    def mask_sensitive_data(self, text: str, user_role: str) -> str:
        masked = text
        
        # 根据用户角色决定脱敏级别
        if user_role == 'internal':
            # 只脱敏高度敏感信息
            masked = self.mask_pattern(masked, 'ssn')
            masked = self.mask_pattern(masked, 'credit_card')
        elif user_role == 'public':
            # 脱敏所有PII
            for pattern_name in self.patterns:
                masked = self.mask_pattern(masked, pattern_name)
        
        return masked
    
    def mask_pattern(self, text: str, pattern_name: str) -> str:
        pattern = self.patterns[pattern_name]
        
        def replacer(match):
            if pattern_name == 'email':
                parts = match.group(0).split('@')
                return f"{parts[0][0]}***@{parts[1]}"
            elif pattern_name == 'phone':
                return f"{match.group(0)[:4]}***{match.group(0)[-4:]}"
            else:
                return '***MASKED***'
        
        return re.sub(pattern, replacer, text)

七、监控与运维

7.1 监控仪表板

javascript 复制代码 
// monitoring-server.js
export class MonitoringServer extends Server {
  constructor() {
    super({ name: 'monitoring', version: '1.0.0' });
    
    this.metrics = {
      tool_calls: new Counter(),
      response_times: new Histogram(),
      errors: new Counter(),
      cache_hits: new Counter()
    };
    
    this.setupMonitoringTools();
  }
  
  setupMonitoringTools() {
    this.setRequestHandler('tools/list', async () => ({
      tools: [
        {
          name: 'get_system_health',
          description: '获取系统健康状态',
          inputSchema: {
            type: 'object',
            properties: {
              detailed: { type: 'boolean', default: false }
            }
          }
        },
        {
          name: 'get_performance_metrics',
          description: '获取性能指标',
          inputSchema: {
            type: 'object',
            properties: {
              time_range: { 
                type: 'string',
                enum: ['1h', '24h', '7d', '30d']
              }
            }
          }
        }
      ]
    }));
  }
  
  async handleGetSystemHealth(args) {
    const health = {
      status: 'healthy',
      timestamp: new Date().toISOString(),
      components: {
        document_extractor: await this.checkServiceHealth('extractor'),
        embedding_server: await this.checkServiceHealth('embedding'),
        knowledge_graph: await this.checkServiceHealth('neo4j'),
        vector_db: await this.checkServiceHealth('chromadb')
      },
      metrics: {
        daily_requests: this.metrics.tool_calls.get('24h'),
        avg_response_time: this.metrics.response_times.avg(),
        error_rate: this.metrics.errors.rate('5m'),
        cache_hit_rate: this.metrics.cache_hits.rate()
      }
    };
    
    // 确定整体状态
    const unhealthy = Object.values(health.components)
      .filter(c => c.status !== 'healthy').length;
    
    if (unhealthy > 0) {
      health.status = unhealthy > 1 ? 'degraded' : 'warning';
    }
    
    return {
      content: [{ type: 'text', text: JSON.stringify(health, null, 2) }]
    };
  }
}

7.2 Prometheus 指标导出

yaml 复制代码 
# prometheus.yml
scrape_configs:
  - job_name: 'mcp-servers'
    static_configs:
      - targets:
        - 'document-extractor:9090'
        - 'embedding-server:9090'
        - 'knowledge-graph:9090'
        - 'recommendation-engine:9090'
    metrics_path: '/metrics'
    
# Grafana 仪表板配置
dashboard:
  panels:
    - title: "请求量"
      query: 'rate(mcp_tool_calls_total[5m])'
    - title: "响应时间"
      query: 'histogram_quantile(0.95, rate(mcp_response_time_seconds_bucket[5m]))'
    - title: "错误率"
      query: 'rate(mcp_errors_total[5m]) / rate(mcp_tool_calls_total[5m])'

八、实际应用案例

案例：法律文档分析系统

python 复制代码 
# legal_analysis_server.py
class LegalDocumentServer(Server):
    async def analyze_contract(self, contract_text):
        # 1. 条款提取
        clauses = await self.extract_clauses(contract_text)
        
        # 2. 风险识别
        risks = await self.identify_risks(clauses)
        
        # 3. 相似案例查找
        similar_cases = await self.find_similar_cases(contract_text)
        
        # 4. 建议生成
        recommendations = await self.generate_recommendations(
            clauses, risks, similar_cases
        )
        
        return {
            "summary": self.generate_summary(clauses),
            "risk_assessment": risks,
            "similar_precedents": similar_cases[:5],
            "recommendations": recommendations,
            "compliance_check": await self.check_compliance(contract_text)
        }
    
    async def extract_clauses(self, text):
        # 使用微调模型识别法律条款
        return await self.call_tool('legal_clause_extractor', {
            "text": text,
            "jurisdiction": "US"
        })

案例：学术文献推荐系统

javascript 复制代码 
// academic_recommender.js
class AcademicRecommender {
  async findRelatedPapers(userPaper) {
    // 1. 基于引用的推荐
    const citationBased = await this.findByCitation(userPaper.citations);
    
    // 2. 基于内容的推荐
    const contentBased = await this.findBySemanticSimilarity(userPaper.abstract);
    
    // 3. 基于作者的推荐
    const authorBased = await this.findByAuthorCollaboration(userPaper.authors);
    
    // 4. 基于研究趋势的推荐
    const trendBased = await this.findByResearchTrends(userPaper.keywords);
    
    return this.rankAndBlendRecommendations([
      citationBased, contentBased, authorBased, trendBased
    ]);
  }
}

九、未来扩展方向

9.1 多模态文档处理

python 复制代码 
# multimodal_server.py
class MultimodalDocumentServer:
    async def process_multimodal(self, file_path):
        # 提取文本内容
        text = await self.extract_text(file_path)
        
        # 提取图像中的文本和信息
        images = await self.extract_images(file_path)
        image_texts = await self.ocr_images(images)
        image_descriptions = await self.describe_images(images)
        
        # 处理表格数据
        tables = await self.extract_tables(file_path)
        table_analysis = await self.analyze_tables(tables)
        
        # 多模态融合
        combined = await this.fuse_modalities(
            text, image_texts, image_descriptions, table_analysis
        )
        
        return combined

9.2 实时协作分析

javascript 复制代码 
// collaborative_analysis.js
class CollaborativeAnalysisServer {
  async realtimeDocumentAnalysis(documentId, userId) {
    // 实时文档更新监听
    const changes = await this.watchDocumentChanges(documentId);
    
    // 协同分析
    const analysis = await this.collaborativeAnalyze(
      documentId,
      [userId, ...await this.getCollaborators(documentId)]
    );
    
    // 实时推荐更新
    const recommendations = await this.updateRecommendations(
      documentId,
      changes,
      analysis
    );
    
    return {
      analysis,
      recommendations,
      collaborators: await this.getActiveCollaborators(documentId)
    };
  }
}

总结

通过 MCP 构建的智能文档分析系统实现了：

  1. 模块化设计 - 每个功能独立为 MCP 服务器，便于维护和扩展
  2. 安全可控 - 细粒度权限控制，数据脱敏处理
  3. 高性能 - 多层缓存、异步处理、批量操作
  4. 智能化 - 语义分析、知识图谱、个性化推荐
  5. 可观测性 - 完善的监控和日志体系

这种架构不仅适用于文档分析，还可以扩展到代码分析、研究助手、企业知识库等多个场景。MCP 的协议标准化使得不同 AI 应用可以无缝集成这些能力，真正实现了 "一次构建，多处使用" 的目标。

开源项目参考：

随着 MCP 生态的成熟，未来会有更多专业化的服务器出现，进一步降低构建智能文档分析系统的门槛。

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