RethinkDB Agent Native Score | Agent Native Registry

Checklist Breakdown

14 of 33 checks passed. 14 unscored.

Discovery 50%

Can an agent find and understand this tool without a web search?

✗ Published OpenAPI/Swagger spec

✗ Has llms.txt or llms-full.txt

✗ Has an MCP server (official or well-maintained)

✗ MCP server listed in a public registry

✓ API reference docs are publicly accessible

✓ Docs include runnable code examples

✓ Has a public changelog or release notes

✓ Has a public status page

Auth & Onboarding 83%

Can an agent create an account and get credentials without human intervention?

✓ Signup does not require CAPTCHA

✓ Signup does not require phone verification

✗ Supports API key auth (not only OAuth)

✓ API key obtainable without manual approval

✓ No mandatory billing info to start

✓ Can sign up without creating an organization

Pricing 100%

Can an agent operate autonomously without upfront payment or contracts?

✓ Has a free tier

✓ Usage-based pricing available

✓ No minimum contract or commitment

✓ Pricing page is public (no 'contact sales')

✓ Free tier sufficient for testing (not just a trial)

Agent Tooling Not yet scored

How well does the API work for non-human consumers?

— SDK available in 2+ languages

— Structured error responses (JSON with error codes)

— Idempotency support on write endpoints

— Pagination on list endpoints

— Webhook/event support

— Sandbox or test mode available

— Rate limit headers in responses

— Consistent REST resource naming

Reliability Not yet scored

Does the tool fail gracefully when an agent makes a mistake?

— Meaningful error messages (not just 500)

— 429 responses include Retry-After header

— Documented uptime SLA (99.9%+)

— Graceful degradation under rate limits

— Request IDs in responses for debugging

— API versioning supported

Reviewer Notes

RethinkDB lacks modern agent discovery mechanisms—no MCP server, OpenAPI spec, or llms.txt file. The self-hosted nature means agents can spin up instances programmatically, but documentation assumes human developers rather than AI agents using the query language. While the ReQL API is well-structured and the project is open-source with good reliability for self-hosted deployments, the absence of REST/HTTP-first design and lack of agent-specific tooling integration significantly limits autonomous agent usage. The project's dormancy in recent years adds uncertainty about production reliability.

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