Assay

The quality layer for agentic software.

Independent agent-friendliness ratings for MCP servers, APIs, and SDKs. Assay scores packages on documentation accuracy, error quality, security posture, and more — so agents (and developers) can pick the right tool for the job.

What it does

Rates 2,400+ packages on a 0-100 AF Score (Agent-Friendliness)
Covers MCP servers, REST APIs, GraphQL services, and SDKs
Scores across 5 dimensions: MCP quality, documentation, error messages, security, and auth complexity
Provides a REST API, MCP server, and web interface

Principles

Agents are first-class citizens — every API, data format, and interface is designed for programmatic consumption by AI agents, not just humans
Independent ratings — vendors cannot buy influence over scores
Trust is the product — if the ratings aren't honest, they're worthless
Built for everyone — agents discovering tools, developers choosing dependencies, and teams evaluating vendors

Quick start

BASH

# Clone and install
git clone https://github.com/assay-tools/assay.git
cd assay
uv sync

# Set up environment
cp .env.example .env
# Edit .env with your settings

# Run locally
uvicorn assay.api.app:app --reload --port 8000

Visit http://localhost:8000 for the web UI, http://localhost:8000/docs for API docs.

API

BASH

# Search packages
curl 'https://assay.tools/v1/packages?q=email&limit=5'

# Get a specific package
curl 'https://assay.tools/v1/packages/resend'

# Agent-optimized guide
curl 'https://assay.tools/v1/packages/resend/agent-guide'

# Compare packages
curl 'https://assay.tools/v1/compare?ids=resend,sendgrid,postmark'

# Stats
curl 'https://assay.tools/v1/stats'

MCP Server

Assay includes an MCP server so AI agents can query ratings at runtime:

JSON

{
  "mcpServers": {
    "assay": {
      "command": "python",
      "args": ["-m", "assay.mcp_server"]
    }
  }
}

Tools: find_packages, get_package, compare_packages, list_categories

Scoring methodology

Each package is evaluated across 5 weighted dimensions:

Dimension	Weight	What it measures
MCP Quality	20%	Tool descriptions, schema completeness, error handling
Documentation	20%	Accuracy, examples, completeness for agent consumption
Error Messages	15%	Actionable errors that help agents self-correct
Security	15%	Auth patterns, input validation, least-privilege design
Auth Complexity	15%	How easy it is for an agent to authenticate

The AF Score is the weighted average, scaled 0-100.

Business Model & Operations

Assay is an agentic business — AI agents handle evaluation, report generation, prospecting, and operational execution. See BUSINESS.md for the full business model, pricing tiers, product streams, and operating philosophy.

License

MIT