Borrowing it
Nothing to install: this file belongs to djmoore711/brandfetch-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/djmoore711/brandfetch-mcp/main/AGENTS.mdgit clone --depth 1 https://github.com/djmoore711/brandfetch-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/djmoore711/brandfetch-mcp/agents-md)<a href="https://agentmods.dev/instructions/djmoore711/brandfetch-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/djmoore711/brandfetch-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 | $0.04778 | $0.04778 |
| Opus 5 | $0.02389 | $0.02389 |
| Sonnet 5 | $0.00956 | $0.00956 |
| Haiku 4.5 | $0.00478 | $0.00478 |
Grade A, and why
brandfetch-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 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.
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 — 752 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Agent Instructions for Brandfetch MCP Server
Project Overview
You are completing a Model Context Protocol (MCP) server that integrates with the Brandfetch API. This server allows AI assistants like Claude to retrieve brand data including logos, colors, fonts, and company information.
Key Facts:
- Language: Python 3.10+
- Framework: MCP Python SDK
- API: Brandfetch REST API (https://api.brandfetch.io/v2/)
- Transport: stdio (standard input/output)
- Purpose: AI model testing and prompt development
Current Implementation Status
✅ Complete
- Project structure and directories
- Core dependencies configured (pyproject.toml)
- API client implementation (client.py)
- MCP server implementation (server.py)
- Basic test framework (test_server.py)
- Documentation files (README, SPEC, API_REFERENCE)
🔨 Your Tasks
- Test the implementation with real API calls
- Enhance error handling and edge cases
- Improve response formatting for Claude
- Add comprehensive tests
- Validate the MCP integration
- Document any issues found
- Suggest improvements
Environment Setup
Prerequisites Check
cd /Users/dj/Code/brandfetch_mcp
# Verify Python version
python3 --version # Should be 3.10+
# Check if uv is installed
which uv || echo "Install uv from: https://astral.sh/uv/install.sh"
Installation Steps
# Create virtual environment
uv venv
# Activate it
source .venv/bin/activate
# Install dependencies
uv pip install -e ".[dev]"
# Verify installation
python -c "import mcp; import httpx; import dotenv; print('All imports successful')"
API Key Setup
# Copy environment template
cp .env.example .env
# User must add their API keys manually
# Edit .env and set:
BRANDFETCH_CLIENT_ID=paste_logo_key_here
BRANDFETCH_API_KEY=paste_brand_key_here
Important: The user must provide their own Brandfetch API keys from https://brandfetch.com/developers
Testing Strategy
Phase 1: Unit Tests
Test the API client in isolation:
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 · 752 lines · 4,778 tokens per session scan A 6a5fe34756db
brandfetch-mcp AGENTS.md is an instructions file published in the GitHub repository djmoore711/brandfetch-mcp (1 stars, last pushed 9mo ago), licensed MIT. It adds 4,778 tokens to every session, about $0.0239 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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