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Aegis MCP Server

by CJH91577

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

🛡️ Aegis — Enterprise-Grade AI Agent Platform

Multi-format document RAG · Multi-agent collaboration · MCP tool invocation · Fact-checking with self-correction · HITL human approval

Aegis (/ˈiːdʒɪs/, Shield) is an out-of-the-box enterprise-grade AI agent application, designed around two main pillars: "Trustworthy Answers" and "Safe Execution":

Answer side: Multi-agent (Planner → Retriever → Auditor → Answerer) division of labor, every answer goes through Auditor fact-checking; when evidence is insufficient, it states so honestly and never fabricates;
Execution side: Tool invocation is based on the standardized MCP (Model Context Protocol), combined with least-privilege whitelist + human approval for sensitive operations (HITL) to achieve secure isolation.

                    ┌──────────────────────────────────────────────┐
                    │                用户 / FastAPI / CLI            │
                    └──────────────────────┬───────────────────────┘
                                           │ 提问
                    ┌──────────────────────▼───────────────────────┐
                    │           Orchestrator（多智能体编排器）        │
                    │                                              │
                    │  ① Planner    规划：拆解子问题 + 工具计划        │
                    │  ② Retriever  检索：混合检索 + MCP 工具执行      │
                    │  ③ Answerer   起草：基于证据、带引用 [C1][C2]   │
                    │  ④ Auditor    核查：逐条声明 vs 证据            │
                    │       └─ revise ─► 带反馈重检/重答（≤3 轮）     │
                    └──────┬──────────────────────────────┬────────┘
                           │ 语义检索                       │ MCP 协议
              ┌────────────▼───────────┐       ┌───────────▼────────────┐
              │  知识库（RAG）           │       │   MCP 工具服务器        │
              │  PDF/DOCX/XLSX/PPTX/TXT│       │  calculator / kb_search │
              │  → 解析 → 切块 → 向量化  │       │  doc_stats / …          │
              │  → Chroma + BM25 混合   │       │  🔒 敏感工具 → HITL 审批 │
              └────────────────────────┘       └────────────────────────┘

✨ Core Capabilities

Requirement	Implementation
Multi-format document knowledge ingestion and semantic retrieval	PDF / Word / Excel / PPT / TXT parsing (including tables, slides, page-number positioning); recursive chunking; vector + BM25 hybrid retrieval (RRF fusion); local embeddings with no API Key required
Multi-agent division of labor	Planner (planning) → Retriever (retrieval + tools) → Answerer (drafting with citations) → Auditor (verification); on audit failure, automatically re-retrieves/answers with feedback, up to N rounds
MCP-based standardized tool invocation with secure isolation	MCP server implemented on the official `mcp` SDK (supports both in-process and standard stdio access); JSON Schema parameter validation; per-agent least-privilege whitelist; AST whitelist calculator to eliminate injection
Auditor fact-checking and self-correction	Verifies each claim against evidence (supported/unsupported/contradicted) line by line, outputs confidence scores and revision suggestions; when evidence is insufficient, explicitly states so and refuses to fabricate, and may autonomously supplement retrieval
HITL human intervention	Sensitive tools (send email/export/delete) trigger approval-request suspension, with execution resuming after approve/reject; three modes: `interactive` / `auto_approve` / `auto_deny` (deny by default, fail-safe)

Related MCP server: Works With Agents MCP Server

🚀 Quick Start

# 1. 安装（Python 3.10+）
cd enterprise-rag-agent
pip install -e .

# 2.（可选）配置 LLM —— 默认离线模式无需配置；接入真实模型见下文
cp .env.example .env   # 填入 OPENAI 兼容的 API Key（OpenAI/DeepSeek/Ollama 等）

# 3. 一键端到端演示（自动生成 5 种格式样例文档 → 入库 → 问答 → 审批，全部自校验）
aegis demo

# 4. 导入你自己的文档
aegis ingest ./your_docs

# 5. 提问（多智能体流水线）
aegis ask "一线城市出差住宿报销上限是多少？"

# 6. 启动 HTTP 服务（Swagger: http://127.0.0.1:8000/docs）
aegis serve

Run the full pipeline with no API Key required: the default embedding model uses a local ONNX (BAAI/bge-small-zh-v1.5, ~95MB auto-downloaded on first run), and the LLM uses a built-in deterministic backend for end-to-end verification; to connect a real model, simply configure it in .env.

Connecting a Real LLM

Supports any OpenAI-compatible protocol endpoint:

# OpenAI / DeepSeek / 通义 / 本地 vLLM
AEGIS_LLM_PROVIDER=openai
AEGIS_LLM_BASE_URL=https://api.deepseek.com/v1
AEGIS_LLM_API_KEY=sk-xxxx
AEGIS_LLM_MODEL=deepseek-chat

# 或本地 Ollama（含嵌入，完全离线）
AEGIS_LLM_PROVIDER=ollama
AEGIS_LLM_MODEL=qwen2.5:3b
AEGIS_EMBED_PROVIDER=ollama
AEGIS_EMBED_MODEL=nomic-embed-text

📖 Documentation

Document	Content
docs/architecture.md	Architecture design, multi-agent collaboration protocol, security model
docs/deploy.md	Deployment, configuration, LLM integration, MCP stdio mode
docs/verification.md	End-to-end self-verification report and verification methods

🔌 HTTP API

Method	Path	Description
POST	`/api/documents/ingest`	Upload document for ingestion (multipart)
POST	`/api/documents/ingest-path`	Batch ingestion by path
GET	`/api/documents`	Document list
DELETE	`/api/documents/{id}`	Delete document (sensitive → HITL)
POST	`/api/chat`	Ask a question (multi-agent pipeline)
GET	`/api/sessions/{id}`	Session status
POST	`/api/sessions/{id}/resume`	Resume session after approval
GET	`/api/approvals`	Approval-request list
POST	`/api/approvals/{id}/decide`	Approve/reject (`{"approve": true}`)
GET	`/api/tools` · POST `/api/tools/call`	MCP tool catalog / manual tool invocation
GET	`/health`	Health check

🧩 MCP Tools

Exposed via the standard Model Context Protocol, accessible by any MCP client:

python -m aegis.mcp.server   # 标准 stdio 服务

Tool	Description	Security Level
`calculator`	AST whitelist safe calculation	✅ Regular
`get_current_time`	Current time	✅ Regular
`kb_search`	Knowledge base semantic retrieval	✅ Regular
`doc_stats`	Knowledge base statistics	✅ Regular
`send_email`	Simulated email sending	🔒 Sensitive → HITL
`export_file`	Export file (sandbox directory)	🔒 Sensitive → HITL
`delete_document`	Delete document	🔒 Sensitive → HITL

🧪 Testing and Verification

pip install -e ".[dev]"
pytest -q              # 单元 + 集成测试（全部离线可跑）
aegis demo             # 端到端演示（含 20+ 条自校验断言）

📁 Directory Structure

enterprise-rag-agent/
├── aegis/
│   ├── llm/            # LLM 抽象层（OpenAI 兼容 / Ollama / 离线确定性）
│   ├── embeddings/     # 嵌入层（fastembed 本地 / OpenAI / Ollama）
│   ├── ingestion/      # 解析（5 格式）→ 切块 → 入库管道
│   ├── retrieval/      # Chroma 向量库 + BM25 混合检索
│   ├── mcp/            # MCP 服务器 / 客户端 / 安全策略
│   ├── agents/         # Planner / Retriever / Answerer / Auditor
│   ├── hitl/           # 人工审批管理器
│   ├── api/            # FastAPI 服务
│   ├── orchestrator.py # 多智能体编排器（状态机 + 挂起恢复）
│   ├── demo.py         # 端到端演示
│   └── cli.py          # 命令行入口
├── scripts/make_sample_docs.py   # 样例文档生成
├── tests/              # pytest 套件
└── docs/               # 架构 / 部署 / 验证文档

⚠️ Security Notes

Sensitive tools are denied by default (auto_deny); for production environments, it is recommended to keep interactive human approval;
export_file only allows writing to the exports/ sandbox under the data directory;
The calculator uses AST whitelist evaluation, with no eval injection surface;
The project's send_email is a simulated implementation; before connecting a real email gateway, implement it yourself and keep HITL approval in place.

📄 License

MIT

Tool Schema Changelog

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