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Contract changes in public APIs

apis_changes
Read-only

Dated contract changes in widely used public APIs (Stripe, GitHub, OpenAI, Twilio, Slack, Plaid, Datadog and more), from their published OpenAPI descriptions read daily: operations, parameters and request fields added, removed or newly required, each change marked breaking or not. Last 1 to 90 days, one API with api=, or only breaking changes with breaking=true. Signed. Price: $0.01 USDC per call via x402 (Base or Solana).

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": "apis_changes"
  }
}

Input Schema

NameRequiredDescriptionDefault
apiNo
daysNo
breakingNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already establish readOnlyHint and openWorldHint, so the safety profile is covered. The description adds genuinely useful behavior: the data source and cadence (published OpenAPI descriptions read daily), the 1-90 day window limit, that responses are signed, and critically the paid model ($0.01 USDC per call via x402 on Base or Solana), which an agent must know before invoking.

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?

Purpose and source scope are front-loaded, followed by filters and then the payment/logistics note. The dense first sentence is information-rich rather than padded, and the pricing sentence earns its place by affecting whether an agent calls at all.

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

Completeness4/5

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

For a read-only, zero-required-param tool with no output schema, the description covers source, freshness, filtering, window limits and cost. The main omission is any indication of the shape of returned change records, but with no output schema that is a modest gap rather than a blocking one.

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?

Schema coverage is 0%, so the description carries the full burden, and it covers all three parameters: 'days' with its 1-90 range, 'api' for selecting one API, and 'breaking=true' for the breaking-only filter. This adds real semantics (value ranges and selection behavior) beyond the bare untyped string properties in the schema.

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 verb+resource (dated contract changes in public APIs) and names the concrete source set (Stripe, GitHub, OpenAI, Twilio, Slack, Plaid, Datadog), which implicitly separates it from siblings like ai_changes and mcp_changes. It does not explicitly name a sibling it is not, so it stops short of a 5.

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

Usage Guidelines3/5

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

It conveys usage constraints ('Last 1 to 90 days, one API with api=, or only breaking changes with breaking=true'), but these are parameter behaviors rather than when-to-use guidance. There is no statement of when to prefer this over ai_changes, mcp_changes, checks_changes or crowd_changes, so routing is left to inference from the domain wording.

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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