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ExchangeRebel — pay-per-call APIs for AI agents (x402 on Solana)

observatory_criteria

The published criteria for the Observatory Aggregates service (observatory-aggregates/v1): the catalogue of snapshots with each counting rule and threshold, the scope label ('our doorway only'), the confidentiality boundary, and the pinned public test corpus identity. Read these before paying - every snapshot is reproducible from them. - no payment required: call directly without payment (no x402 challenge; this tool is free). HTTP equivalent: GET https://exchangerebel.com/toll/observatory/criteria.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "observatory_criteria"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are supplied, so the description carries the full burden and does well: it discloses that the call is free with no x402 challenge, that no payment is required, and what the returned material covers. The main undocumented behavior is the response shape and size (there is no output schema), which the prose only broadly sketches.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with what the criteria contain, then the 'read before paying' instruction, then the free-call note and HTTP equivalent. The parenthetical protocol version and endpoint line are useful rather than bloated, though the enumerated-content sentence is dense.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter, free, read-only criteria endpoint with no output schema, the description covers everything an agent needs: what it returns, why to call it first, that it costs nothing, and a fallback HTTP path. Nothing material is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, so the baseline is 4. The description correctly implies no inputs are needed by presenting the call as a direct free GET.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific resource (the published criteria for observatory-aggregates/v1) and enumerates what it contains: snapshot catalogue, counting rules, thresholds, scope label, confidentiality boundary, and a pinned test corpus identity. The sibling observatory_snapshot is not named directly, but 'read these before paying' implies the criteria/snapshot relationship, so it is clearer than a generic 3 but short of explicit sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives a clear use condition: read this before paying/calling the snapshot tool, because every snapshot is reproducible from these criteria. It does not explicitly name the alternative tool or say when not to use it, which keeps it out of 5 territory.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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