Borrowing it
Nothing to install: this file belongs to mm-repos/langgraph-claude-azure-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/mm-repos/langgraph-claude-azure-mcp/main/.github/copilot-instructions.mdgit clone --depth 1 https://github.com/mm-repos/langgraph-claude-azure-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/mm-repos/langgraph-claude-azure-mcp/copilot-instructions)<a href="https://agentmods.dev/instructions/mm-repos/langgraph-claude-azure-mcp/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/mm-repos/langgraph-claude-azure-mcp/copilot-instructions/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/mm-repos/langgraph-claude-azure-mcp/copilot-instructions"><img src="https://agentmods.dev/badge/instructions/mm-repos/langgraph-claude-azure-mcp/copilot-instructions.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.02663 | $0.02663 |
| Opus 5 | $0.01332 | $0.01332 |
| Sonnet 5 | $0.00533 | $0.00533 |
| Haiku 4.5 | $0.00266 | $0.00266 |
Grade A, and why
langgraph-claude-azure-mcp copilot-instructions.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 242 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure AI Search MCP Server
Project Overview
This project is a Model Context Protocol (MCP) server that provides seamless integration with Azure AI Search services. The server enables AI assistants and applications to perform intelligent search operations across indexed documents and data sources.
Key Technologies
- Python 3.8+ - Core development language
- LangChain/LangGraph - AI workflow orchestration and chaining
- Azure AI Search - Cloud-based search service integration
- Google Gemini - Large language model for enhanced document processing
- MCP Protocol - Standard protocol for AI model context sharing
- LangSmith - Observability and tracing for AI chain executions
- Pydantic - Type-safe configuration validation and data models
- python-dotenv - Environment variable management and secure secrets handling
Core Functionality
- Document Search: Intelligent search across Azure AI Search indexes
- Chain Processing: LangGraph-based workflow execution for complex queries
- AI-Enhanced Processing: Google Gemini integration for document formatting, summarization, and analysis
- MCP Compliance: Full Model Context Protocol implementation
- Prompt Management: Dynamic prompt templating and management system
- Real-time Integration: Live connection to Azure AI Search services
- Observability: Comprehensive tracing and monitoring with LangSmith
Architecture Components
azure_search.py- Azure AI Search client and operationschain.py- LangGraph chain definitions and workflow logicserver.py- MCP server implementation and protocol handlersprompt_manager.py- Dynamic prompt template managementconfig.py- Configuration management and environment setup
Development Status
- ✅ Core MCP server functionality
- ✅ Azure AI Search integration
- ✅ Google Gemini integration with proper LangChain chains
- ✅ LangGraph chain processing
- ✅ LangSmith tracing and observability
- ✅ Prompt management system
- ✅ Comprehensive testing suite
- ✅ Pydantic-based configuration validation
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 242 lines · 2,663 tokens per session scan A 29a15bfa8114
langgraph-claude-azure-mcp copilot-instructions.md is an instructions file published in the GitHub repository mm-repos/langgraph-claude-azure-mcp (0 stars, last pushed 1y ago), licensed MIT. It adds 2,663 tokens to every session, about $0.0133 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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