Borrowing it
Nothing to install: this file belongs to vishnu2kmohan/mcp-server-langgraph. 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/vishnu2kmohan/mcp-server-langgraph/main/.cursorrulesgit clone --depth 1 https://github.com/vishnu2kmohan/mcp-server-langgraphWrote 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/rules/vishnu2kmohan/mcp-server-langgraph/cursorrules)<a href="https://agentmods.dev/rules/vishnu2kmohan/mcp-server-langgraph/cursorrules"><img src="https://agentmods.dev/badge/rules/vishnu2kmohan/mcp-server-langgraph/cursorrules/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/rules/vishnu2kmohan/mcp-server-langgraph/cursorrules"><img src="https://agentmods.dev/badge/rules/vishnu2kmohan/mcp-server-langgraph/cursorrules.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 | $0.02038 | $0.02038 |
| Opus 5.5 | $0.00815 | $0.00815 |
| Sonnet 5.5 | $0.00408 | $0.00408 |
| Haiku 4.5 | $0.00204 | $0.00204 |
Grade A, and why
cursorrules 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 1mo 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 — 358 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cursor AI Rules for LangGraph MCP Agent
Project Overview
This is a production-ready LangGraph agent with Model Context Protocol (MCP) implementation, featuring:
- Multi-LLM support via LiteLLM (100+ providers)
- Fine-grained authorization with OpenFGA
- Secrets management with Infisical
- OpenTelemetry observability
- Kubernetes-ready deployment
Code Style Guidelines
Python
- Line length: 127 characters (enforced by black)
- Formatter: black with
--line-length=127 - Import sorting: isort with
--profile=black - Type hints: Always use type hints for function signatures
- Docstrings: Use Google-style docstrings
- Naming conventions:
- Classes: PascalCase
- Functions/methods: snake_case
- Constants: UPPER_SNAKE_CASE
- Private methods: _leading_underscore
File Organization
- Keep files focused and under 500 lines when possible
- Group related functionality together
- Use clear, descriptive file names
Architecture Patterns
Agent Design
- Use LangGraph's functional API for stateless operations
- Implement proper state management with checkpointing
- Keep agent logic separate from transport layer (MCP)
Authentication & Authorization
- Always validate JWT tokens before processing requests
- Use OpenFGA for fine-grained authorization checks
- Never hardcode credentials or secrets
- Use Infisical or environment variables for secrets
Error Handling
- Use specific exception types
- Log errors with trace context
- Return user-friendly error messages
- Never expose internal stack traces to clients
Observability
- Add OpenTelemetry spans for all major operations
- Use structured logging with context
- Track metrics for performance-critical paths
- Include trace IDs in all logs
Testing Guidelines
Test Structure
- Unit tests: Mark with
@pytest.mark.unit - Integration tests: Mark with
@pytest.mark.integration - E2E tests: Mark with
@pytest.mark.e2e - Benchmarks: Mark with
@pytest.mark.benchmark
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.
- 1mo ago First seen · 358 lines · 2,038 tokens per session scan A fe8fa8d4d510
cursorrules is a cursor rule published in the GitHub repository vishnu2kmohan/mcp-server-langgraph (4 stars, last pushed 2d ago), licensed MIT. It adds 2,038 tokens to every session, about $0.0082 per session on Opus 5.5. 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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