Borrowing it
Nothing to install: this file belongs to AbhishekMore-1/litellm-proxy-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/AbhishekMore-1/litellm-proxy-mcp/main/AGENTS.mdgit clone --depth 1 https://github.com/AbhishekMore-1/litellm-proxy-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/abhishekmore-1/litellm-proxy-mcp/agents-md)<a href="https://agentmods.dev/instructions/abhishekmore-1/litellm-proxy-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/abhishekmore-1/litellm-proxy-mcp/agents-md/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/abhishekmore-1/litellm-proxy-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/abhishekmore-1/litellm-proxy-mcp/agents-md.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 | $0.00698 | $0.00698 |
| Opus 5 | $0.00349 | $0.00349 |
| Sonnet 5 | $0.00140 | $0.00140 |
| Haiku 4.5 | $0.00070 | $0.00070 |
Grade A, and why
litellm-proxy-mcp AGENTS.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 9d 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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Antigravity AI Agent Instructions
Welcome to the litellm-proxy-mcp project. This document provides instructions for AI coding assistants (like Antigravity) working in this repository.
Project Overview
This is a Model Context Protocol (MCP) server written in Node.js (litellm-mcp-server.js). It connects an MCP client (such as Antigravity or Claude Desktop) to a deployed LiteLLM proxy instance.
- Primary Language: JavaScript (Node.js)
- Architecture:
- Uses
@modelcontextprotocol/sdkto define the MCPServerand capabilities. - Communicates via
stdiotransport. - Forwards
CallToolrequests to the configuredLITELLM_BASE_URLusing nativefetch.
- Uses
Available Tools
The server currently implements the following tools, mapping directly to LiteLLM Proxy endpoints:
chat_completion(/chat/completions)completion(/completions)list_models(/models)health_check(/health)create_embedding(/embeddings)model_info(/model/info)create_image(/images/generations)create_speech(/audio/speech)rerank(/rerank)key_generate(/key/generate)key_info(/key/info)
Development Guidelines
- Single File Approach: The core logic is intentionally kept in
litellm-mcp-server.jsfor simplicity. - Error Handling: Ensure that errors from the LiteLLM API are caught and returned clearly within the MCP tool execution response (
isError: true). - No External Fetch Libraries: We use native Node.js
fetch(requires Node 18+). Do not installaxiosornode-fetch. - Binary Data Handling: Some LiteLLM endpoints (like
/audio/speech) return binary data (e.g., MP3 audio) or images. ThemakeLiteLLMRequestfunction is designed to convertaudio/*andimage/*responses into Base64 encoded strings to pass over MCP.
Testing Locally
We use the native node:test runner. To run the test suite and verify the MCP tool registrations:
npm test
To run the server manually, you need to provide environment variables:
LITELLM_BASE_URL="https://your-litellm-url.com" LITELLM_API_KEY="sk-..." npx litellm-mcp-server
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.
- 9d ago First seen · 56 lines · 698 tokens per session scan A 1696e2da3902
litellm-proxy-mcp AGENTS.md is an instructions file published in the GitHub repository AbhishekMore-1/litellm-proxy-mcp (6 stars, last pushed 6mo ago), licensed MIT. It adds 698 tokens to every session, about $0.0035 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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