Agent Integration

Call the audit API

The production API host is https://api.machineread.ai. Send JSON to the full endpoint when you need every check row:

POST https://api.machineread.ai/v1/audit
Content-Type: application/json

{"url":"https://example.com","preset":"blog"}

The full response includes scores, per-check findings, benchmark context, scope, and strict agent readiness. For a smaller agent-oriented projection, call POST https://api.machineread.ai/v1/audit/summary with the same request body. Neither endpoint uses an LLM to write the result.

Minimal agent workflow

1. Choose the closest valid preset.
2. POST the public URL to the API host.
3. Parse overall_score, benchmark context, and agent_readiness.
4. For the full endpoint, summarize applicable checks and keep its caveats.
5. Treat locked rows as unverified coverage, not failures.

Discovery surfaces

The www static host publishes llms.txt, llms-full.txt, OpenAPI, an RFC 9264 API Catalog linkset, an ARD ai-catalog.json, an MCP Server Card, and an Agent Skills index. The API host also serves the canonical OpenAPI document and API Catalog route. These files describe or link to executable endpoints; their presence does not itself prove live uptime or agent invocation.

• Product guide: https://www.machineread.ai/llms.txt
• ARD: https://www.machineread.ai/.well-known/ai-catalog.json
• API Catalog: https://api.machineread.ai/.well-known/api-catalog
• OpenAPI: https://api.machineread.ai/openapi.json
• MCP Server Card: https://www.machineread.ai/.well-known/mcp/server-card.json
• Agent Skills: https://www.machineread.ai/.well-known/agent-skills/index.json

Hosted MCP server

MachineRead runs an anonymous, rate-limited Streamable HTTP MCP server at https://api.machineread.ai/.well-known/mcp/mcp. It exposes four read-only tools:

• run_essentials_audit(url, preset?, custom_overrides?)
• get_audit_report(audit_id)
• list_available_checks()
• explain_check(check_name)

run_essentials_audit waits for completion and returns a compact MCP projection with audit ID, scores, check count, strict readiness, rate-limit state, and caveat. It does not return the REST endpoint's full per-check evidence array. The same FastMCP instance is also available over optional local stdio with python -m mcp_server.server.

Agent-readiness scoring

The separate strict score evaluates explicit agent-native discovery and protocol signals. Its denominator follows the resolved family scope: named general presets use 8 probes, SaaS uses 17, and Ecommerce uses 21. Custom and partial scopes derive their own exact denominator. Retrieval or metadata observations do not prove ranking, indexing, model citation, crawler identity, or protocol use.