Borrowing it
Nothing to install: this file belongs to sushilti80/datadog-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/sushilti80/datadog-mcp/main/AGENTS.mdgit clone --depth 1 https://github.com/sushilti80/datadog-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/sushilti80/datadog-mcp/agents-md)<a href="https://agentmods.dev/instructions/sushilti80/datadog-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/sushilti80/datadog-mcp/agents-md.svg" alt="Measured on agentmods" 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 | $0.06488 | $0.06488 |
| Opus 5 | $0.03244 | $0.03244 |
| Sonnet 5 | $0.01298 | $0.01298 |
| Haiku 4.5 | $0.00649 | $0.00649 |
Grade A, and why
datadog-mcp AGENTS.md scanned grade A with 1 finding 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 6d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST http://localhost:8080/mcp/ \ How it starts
The opening of the file, as written. The whole thing — 867 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🤖 Agent Task Guidelines - Datadog MCP Server
This document provides specialized guidance for AI agents working on specific tasks for the Datadog MCP Server.
🚀 Quick Start: How to Use This Guide
Step 1: Identify What You Want to Do
Pick ONE task from this list:
- ✅ Write tests → Go to Testing Agent
- ✅ Add a new tool (API endpoint) → Go to Tool Builder Agent
- ✅ Create a resource (AI-friendly data view) → Go to Resource Builder Agent
- ✅ Build a prompt (AI workflow) → Go to Prompt Builder Agent
- ✅ Update documentation → Go to Documentation Agent
- ✅ Debug an issue → Go to Debug & Troubleshooting Agent
Step 2: Read Your Agent's Mission
Each agent section starts with a clear Mission statement telling you what to do.
Step 3: Copy the Template
Each agent provides a template - copy it and fill in the blanks with your specific details.
Step 4: Follow the Checklist
Go through the checklist line-by-line. Check off each item as you complete it. Don't skip any!
Step 5: Validate with Commands
Each agent provides test commands and success criteria to verify your work is correct.
📋 Table of Contents
- Testing Agent
- Tool Builder Agent
- Resource Builder Agent
- Prompt Builder Agent
- Documentation Agent
- Debug & Troubleshooting Agent
💡 Real-World Examples
Example 1: I Want to Add a New Tool
Scenario: You want to add a tool called get_slos() to fetch Service Level Objectives.
- Go to Tool Builder Agent section
- Copy the Tool Template (the
@mcp.toolPython code) - Replace
new_datadog_toolwithget_slos - Replace the docstring and logic with SLO API calls
- Go through Tool Building Checklist - check off each item
- Run test:
pytest tests/test_datadog_mcp_server.py -v - Update
docs/API.mdwith your new tool - Done! Your tool is now available to AI agents
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.
- 6d ago First seen · 867 lines · 6,488 tokens per session scan A fb2fb31e65c0
datadog-mcp AGENTS.md is an instructions file published in the GitHub repository sushilti80/datadog-mcp (2 stars, last pushed 2mo ago), licensed MIT. It adds 6,488 tokens to every session, about $0.0324 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other instructions, from other repositories
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.