Discovery surfaces

Endpoints that describe oculr itself. Call them once at startup to learn the rest of the API. All return 200 and are free - no payment required.

Use from an agent

One prompt hands an agent the whole surface - it reads the discovery doc, learns the call shape, and dispatches:

claude -p "Use the oculr MPP at https://mpp.oculr.xyz/SKILL.md to analyse EVM transaction 0xYOUR_TX_HASH on Ethereum - what happened, any risks?"

For a broader working context (the entire docs corpus, not just the task instructions), point the agent at llms-full.txt instead: "Fetch https://mpp.oculr.xyz/llms-full.txt for complete oculr context, then …".

GET /openapi.json

Machine-readable OpenAPI 3.1 definition of every endpoint, schema, and security scheme. Drop into any OpenAPI client generator (openapi-typescript-codegen, openapi-generator, etc.).

curl https://mpp.oculr.xyz/openapi.json | jq '.info'

{
  "title": "oculr",
  "version": "1"
}

GET /tool-spec.json

Typed Anthropic + OpenAI tool-call schemas. Drop the appropriate array straight into your LLM's tool-use call - no markdown parsing.

Shape

{
  "version": 1,
  "baseUrl": "https://mpp.oculr.xyz",
  "auth": "mpp-x402",
  "anthropic": [ /* 3 tools - Anthropic Messages format */ ],
  "openai":    [ /* 3 tools - OpenAI Chat Completions format */ ],
  "endpoints": {
    "explain_transaction": { "method": "POST", "path": "/explain" },
    "start_explain_job":   { "method": "POST", "path": "/explain/async" },
    "get_job_result":      { "method": "GET",  "path": "/result/{jobId}" }
  },
  "skillUrl":   "https://mpp.oculr.xyz/SKILL.md",
  "openapiUrl": "https://mpp.oculr.xyz/openapi.json"
}

Tools exposed

Use it

const spec = await fetch('https://mpp.oculr.xyz/tool-spec.json').then(r => r.json())
 
const response = await anthropic.messages.create({
  model: 'claude-opus-5',
  tools: spec.anthropic,
  messages: [{ role: 'user', content: 'Analyse tx 0x4e4b8ed4…' }],
})

Full integration walkthrough at Use as an agent → Tool-use mode.

GET /SKILL.md

Prose entry point for autonomous coding agents. The agent fetches it once at startup, learns the call shape, and dispatches with an MPP client.

Use cases:

• Interactive CLIs (Claude Code, Amp, Codex CLI) where a human asks an agent to look at a transaction.
• Persistent skill installation - save it into your agent's skills directory (for Claude Code, ~/.claude/skills/oculr/SKILL.md).

See Use as an agent → Skill mode.

GET /llms.txt

Compact discovery index - one line per doc, designed for LLM crawlers and first-fetch context.

curl https://mpp.oculr.xyz/llms.txt

GET /llms-full.txt

Concatenated full doc corpus. Use when an LLM crawler wants the entire documentation surface in one fetch.

curl https://mpp.oculr.xyz/llms-full.txt

Related

• Endpoints overview
• Use as an agent - SKILL.md vs tool-spec.json decision guidance