Resend is Good to agents.

Item: Resend API
Rating: 89
Author: Discry

Discry independently scored how well an AI agent can discover and understand the Resend API from what’s public — not whether it’s usable. Below: every signal we checked, what’s costing the score, and what to change.

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SCORED UNDER RUBRIC 1.2 · A full re-launch under Discry Score 2.5 — a new behavioral instrument, not comparable to these scores — is in progress.

Discovery

45% of score · 81/100

OpenAPI specA machine-readable OpenAPI/Swagger spec agents can parse.Pass

llms.txtAn llms.txt index that points agents to the docs that matter.Pass

llms.txt qualityThe llms.txt is focused, current, and well under the size budget.Pass

llms-full.txtA full-text bundle agents can load in one request.Fail

AGENTS.mdAn AGENTS.md that tells coding agents how to build on the API.Fail

.well-known/mcp.jsonA discoverable MCP manifest at a well-known path.Pass

MCP registryThe API is listed in a public MCP registry.Pass

robots.txt AI directivesrobots.txt allows (or explicitly guides) AI crawlers.Pass

SitemapA sitemap so agents can enumerate the docs surface.Pass

Comprehension

55% of score · 96/100

Task-oriented descriptionsEndpoints described by what they accomplish, not just their shape.Pass

Realistic examplesRunnable, real-world request/response examples.Pass

Multi-step workflowsDocs that chain calls into complete jobs an agent can follow.Pass

Error-recovery guidanceDocumented failure modes and how to recover from them.Pass

Answer-first formatThe answer leads; preamble does not bury it.Pass

Capability boundariesClear limits — what the API can and cannot do.Partial

Naming consistencyConsistent, predictable naming across endpoints.Pass

Heading hierarchyClean heading structure agents can navigate.Pass

Markdown docsDocs available as clean markdown, not JS-rendered HTML only.Pass

Token efficiencyDocs are concise enough to fit an agent context window.Pass

What we found

Resend remains one of the most agent-ready APIs: llms.txt, .well-known/mcp.json with 12 tool declarations, OpenAPI spec, and Agent Skills all present and well-structured
Error documentation is exemplary for agents — every error code includes a 'Suggested action' with specific recovery steps, making autonomous error handling straightforward
The llms.txt is optimally sized at 2.3KB with structured sections linking to MCP server, Skills, OpenAPI spec, SDKs, and documentation — an agent can understand the full API surface in seconds
No AGENTS.md in GitHub repos and no llms-full.txt at root domain — these are the only two discovery signals missing
Multi-step workflow coverage is strong with framework-specific guides (Next.js, Remix, Express, etc.) showing install → template → send → verify patterns

What to change

Prioritized by impact on discoverability. You (or your docs platform) deploy these — Discry never touches your API.

01Add AGENTS.md to the resend/resend-node or resend/resend GitHub repo with context for coding agents working with the SDK
02Add llms-full.txt at resend.com/llms-full.txt containing the complete API documentation in markdown (docs/llms.txt exists but root-level full version does not)
03Document specific per-endpoint rate limits beyond the default 5 req/s to help agents plan batch operations

Execution coverage · INFORMATIONAL, UNSCORED

Whether an agent can actually complete a call and recover from errors is the deeper Audit layer — documented here, but not part of the Discry Score.

api_key✓ Error format documented✓ Rate limits documented✓ Pagination documented✓ Idempotency documented

Comprehensive execution documentation. API key auth (Bearer token), JSON error responses with typed error codes and suggested actions, rate limits documented (5 req/s default), cursor-based pagination, and Idempotency-Key header support with 24hr expiry.

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