◎ Discry Score
smartlead.com
communication · API
B
0 / 100
DISCOVERY0
COMPREHENSION0
Category leader: 98 (A)
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COMMUNICATION · RANK #6 OF 24

Smartlead is Good to agents.

Discry independently scored how well an AI agent can discover and understand the Smartlead 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 · 71/100
OpenAPI specA machine-readable OpenAPI/Swagger spec agents can parse.Partial
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.Pass
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.Partial
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 · 100/100
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.Pass
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

  • An agent discovering Smartlead lands well: robots.txt on the docs host carries an explicit Content-Signal (ai-train=yes, search=yes, ai-input=yes), and a clean, API-focused llms.txt maps every guide, core concept, and endpoint group — the API is understandable in under ~2,500 tokens before any page is opened.
  • Comprehension is essentially flawless. Endpoint pages lead with the method, URL, and a task-oriented purpose, ship copy-pasteable cURL/Python/JS/PHP examples with realistic values, and consistently use snake_case — an agent can go from zero to a live campaign by following the docs alone.
  • Error recovery is best-in-class for agent use: a dedicated guide pairs each common error (401, 400, 404, 409, 429, 5xx) with an explicit fix, exponential-backoff-with-jitter code, and Retry-After handling — the docs tell an agent what to DO, not just what broke.
  • The single biggest gap is the OpenAPI spec: the Mintlify 'Try it' reference is generated from an underlying schema, but no machine-readable spec is downloadable at any standard path (openapi.json, openapi.yaml all 404) — an agent that wants to auto-generate a client can't fetch one.
  • There is no AGENTS.md in a Smartlead-owned repo, and the vendor-linked .well-known/mcp.json points at a non-canonical Mintlify branch-preview URL (smartlead.main-kill-isr.mintlify.me) rather than a stable production MCP endpoint — a coding agent following it could hit a dead or unstable server.

What to change

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

  1. 01Publish a downloadable OpenAPI spec at a discoverable path (e.g. https://api.smartlead.ai/openapi.json) and link it from llms.txt. This is the highest-weighted discovery signal and the only comprehension-adjacent gap — it lets agents auto-generate typed clients instead of scraping HTML.
  2. 02Fix the .well-known/mcp.json URL to point at a stable production MCP endpoint (not a Mintlify preview subdomain), so agents that resolve the manifest connect to a durable server.
  3. 03Add an AGENTS.md to the primary public Smartlead repo (or publish an official, clearly-branded MCP server repo with one) — the ecosystem already has 6+ community MCP servers, so an authoritative agent-facing entry point would consolidate discovery.
  4. 04Document pagination explicitly (cursor vs. offset, page-size limits, response envelope) for the list endpoints (campaigns, leads, inbox) so agents can iterate large result sets deterministically.
  5. 05Document idempotency support (or its absence) for write endpoints like campaigns/create and lead import, so agents can safely retry after a 5xx without creating duplicates.

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 (query parameter)API key (request body) Error format documented Rate limits documented Pagination documented Idempotency documented

Execution characteristics are documented unusually well for a scan-tier API. Errors return a consistent JSON envelope (error.code, error.message, error.details) and are mapped to HTTP status codes with specific recovery steps. Rate limits are published per plan tier with X-RateLimit-* headers and a Retry-After field. Auth is a single API-key model passed via query parameter or request body. Pagination and idempotency-key support are not documented — an agent would have to infer them or discover them by trial.

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