DigitalOcean is Good to agents.
Discry independently scored how well an AI agent can discover and understand the DigitalOcean API from what’s public — not whether it’s usable. Below: every signal we checked, what’s costing the score, and what to change.
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 · 81/100Comprehension
55% of score · 71/100What we found
- DigitalOcean has one of the most agent-friendly discovery setups scanned — llms.txt with structured JSON index, chunked llms-full.txt to avoid timeout issues, and markdown mirrors for every page via index.html.md suffix
- The robots.txt explicitly allows its own DigitalOceanGenAICrawler and provides Llms-txt/Llms-index directives, signaling active investment in AI discoverability
- An official MCP server exists (digitalocean-labs/mcp-digitalocean) and is listed on PulseMCP and Glama, making agent integration straightforward
- No AGENTS.md exists in any primary DigitalOcean GitHub repo despite having 200+ public repos — a missed signal for coding agents building on the DO API
- Documentation is comprehensive but spread across 4 separate APIs (main API, Spaces, OAuth, Metadata) which increases token cost for agents needing to understand the full platform
What to change
Prioritized by impact on discoverability. You (or your docs platform) deploy these — Discry never touches your API.
- 01Add .well-known/mcp.json pointing to the official mcp-digitalocean server and tool declarations
- 02Add AGENTS.md to digitalocean/openapi or digitalocean/pydo repos with context for coding agents building DO integrations
- 03Create a focused API quickstart guide that chains common workflows (create Droplet -> configure firewall -> add DNS -> deploy app) to help agents understand multi-step patterns
- 04Add idempotency key support documentation to help agents safely retry 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.
Well-documented execution characteristics. Personal access token and OAuth2 auth. Rate limits documented (250 requests per hour for most endpoints). Cursor-based pagination. JSON error responses with id, message, and request_id fields. No idempotency key support documented.