Uses Google Gemini models (e.g., gemini-2.5-flash) as an LLM provider for capabilities such as summarization, RAG answer generation, and AI architecture design.
Uses OpenAI's models (e.g., gpt-4o-mini) as an LLM provider to power document summarization, grounded RAG, LLM evaluation, and other AI-driven tools.
Click on "Deploy 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., "@Enterprise AI Toolkit MCP Analyze the uploaded contract for hidden risks"
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.
An enterprise MCP server that exposes reusable AI, RAG, document intelligence, knowledge graph, SQL, evaluation, and AI architecture capabilities as standardized tools for AI agents.
The Enterprise AI Toolkit MCP acts as an enterprise capability gateway between AI agents (Claude, Gemini, OpenAI, custom agents) and backend enterprise AI infrastructure. Instead of coupling an agent to monolithic custom code or fragile API calls, agents can discover and compose standardized tools for document processing, RAG research, knowledge graph queries, natural language SQL, model evaluation, and cloud architecture design.
┌───────────────────────┐
│ AI Agent │
│ Claude / Gemini / │
│ OpenAI / Custom Agent │
└───────────┬───────────┘
│
│ MCP JSON-RPC
▼
┌──────────────────────────────┐
│ Enterprise AI Toolkit MCP │
│ │
│ MCP Server + Tool Registry │
└──────────────┬───────────────┘
│
┌────────────────────────┼────────────────────────┐
│ │ │
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ Document Tools │ │ Knowledge Tools │ │ Data Tools │
├─────────────────┤ ├─────────────────┤ ├─────────────────┤
│ OCR & Extract │ │ Vector Search │ │ SQL Generation │
│ Summarization │ │ Grounded RAG │ │ SQL Validation │
│ Contract Audit │ │ Knowledge Graph │ │ Execute Query │
└─────────────────┘ └─────────────────┘ └─────────────────┘
┌────────────────────────┼────────────────────────┐
│ │ │
▼ ▼ ▼
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ LLM Tools │ │ Evaluation │ │ Architecture │
├─────────────────┤ ├─────────────────┤ ├─────────────────┤
│ LLM Evaluation │ │ Faithfulness │ │ AI Architecture│
│ Cost Estimator │ │ Groundedness │ │ TCO Cost Calc │
│ Prompt Guard │ │ Latency & Cost │ │ Tech Stack │
└─────────────────┘ └─────────────────┘ └─────────────────┘Related MCP server: production-grade-mcp-agentic-system
16 Standardized MCP Tools: Complete coverage for Document Intelligence, RAG, Knowledge Graph, SQL Intelligence, LLM Evaluation, Cost Estimation, and Cloud Architecture.
100% Demo Mode / Zero API Key Requirement: Runs out of the box using intelligent offline MockLLMProvider, local vector index, in-memory Knowledge Graph, and SQLite database.
LLM Provider Agnostic: Easily toggle between Google Gemini (gemini-2.5-flash), OpenAI (gpt-4o-mini), and local mock provider.
Enterprise Security & Governance:
PromptGuard: Prompt injection threat detection and token efficiency analysis.
SQLGuard: Enforces read-only execution, blocks multi-statements, and catches dangerous keywords (DROP, DELETE, TRUNCATE).
InputValidator: Path traversal protection and upload size guards.
MCP Resources & Prompts: Exposes resource://enterprise-ai/catalog, configuration, model-registry, tool-metrics, and standardized prompts (enterprise_rag_analysis, enterprise_sql_analysis, enterprise_contract_review, enterprise_ai_architecture).
Category | Tool Name | Description | Risk Level |
Documents |
| Extracts text, pages, metadata, headings, and chunks | LOW |
Documents |
| Generates executive summaries, key takeaways, and action items | LOW |
Documents |
| Audits legal contracts for terms, obligations, risks, and risk score | MEDIUM |
Knowledge |
| Semantic vector search over enterprise document chunks | LOW |
Knowledge |
| Grounded RAG research with source citations | LOW |
Knowledge |
| Queries and traverses entity-relationship Knowledge Graph | LOW |
Data |
| Translates natural language into safe SQL queries | MEDIUM |
Data |
| Validates SQL syntax against security policies and injection | LOW |
Data |
| Safely executes read-only SQL queries against database | MEDIUM |
Data |
| Inspects database schema, foreign keys, and optimization tips | LOW |
LLM |
| Evaluates LLM responses across correctness, relevance, faithfulness | LOW |
LLM |
| Estimates daily, monthly, and annual LLM pricing | LOW |
LLM |
| Analyzes prompt for security risks and injection threats | LOW |
LLM |
| Recommends optimal LLM based on budget, latency, and privacy | LOW |
Architecture |
| Generates enterprise AI architecture design with Mermaid diagram | LOW |
Architecture |
| Calculates TCO for enterprise GenAI solution | LOW |
pip install -e .# List all 16 registered tools
enterprise-ai-mcp list-tools
# Check server health
enterprise-ai-mcp health
# Run E2E BFSI Demo Workflow
enterprise-ai-mcp demoenterprise-ai-mcp server --transport stdioThe repository includes a unit and integration test suite runnable without external API keys:
python -m pytest# Docker Compose
docker-compose up -d
# Docker CLI
docker build -t enterprise-ai-mcp .
docker run -p 8000:8000 enterprise-ai-mcpDistributed under the MIT License.
This server cannot be deployed
MCP server for building and testing AI agents with multi-model experimentation and insights.
MCP server connecting AI agents to 100+ apps (Gmail, Slack, Notion, GitHub) via one-click OAuth.
MCP gateway with runtime security policy, tool-call-level control, and audit of agent actions.
The Remote MCP server acts as a standardized bridge between LLM applications (like Claude, ChatGPT, and Cursor) and external services, enabling AI agents to access external tools and resources. Its primary capability is providing a centralized search tool to discover other MCP servers and their respective tools. Unlike local implementations, it runs remotely with OAuth authentication and permission controls for security.
