Linear is Poor to agents.

Item: Linear API
Rating: 55
Author: Discry

Discry independently scored how well an AI agent can discover and understand the Linear 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 · 52/100

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.Partial

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.Fail

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

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

SitemapA sitemap so agents can enumerate the docs surface.Partial

Comprehension

55% of score · 57/100

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

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

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

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

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

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.Partial

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

What we found

Linear has an llms.txt at linear.app but its content is product-focused (docs for using Linear app) rather than API-focused — an agent looking to integrate would find app documentation, not API patterns
The OpenAPI spec at developers.linear.app returns 200, and GraphQL schema is also accessible — good programmatic discoverability
No .well-known/mcp.json despite having an official MCP server listed on PulseMCP — agents must discover integration points externally
robots.txt at developers.linear.app returns an HTML page rather than proper text file — technically a fail for automated discovery
Strong MCP ecosystem presence with official server and 10+ community implementations on Glama

What to change

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

01Create an API-focused llms.txt at developers.linear.app/llms.txt covering the GraphQL API, authentication, and common query patterns
02Add .well-known/mcp.json with tool declarations for issue management, project queries, and team operations
03Fix robots.txt at developers.linear.app to return proper text format instead of HTML page
04Add multi-step workflow guides showing common agent patterns: create issue → assign → update status → add comment
05Add error recovery documentation with specific guidance for GraphQL complexity errors and rate limits

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_keyoauth2✓ Error format documented✓ Rate limits documented✓ Pagination documented✕ Idempotency documented

GraphQL API with API key and OAuth2 auth. Rate limits documented with complexity-based system. Cursor-based pagination (Relay-style connections). Error responses follow GraphQL error format. No idempotency support documented.

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