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engine_schema

Retrieve request and response JSON schemas for a specified pricing engine endpoint to construct valid payloads and prevent errors from omitted required fields.

Instructions

Request and response JSON schema for one engine endpoint.

Args: endpoint: one of the 24 POST paths, e.g. /price-ois-swap (leading slash optional). Unknown names return an error that lists the valid endpoints. depth: how many levels of $ref to inline (0..8, default 3). Deeper refs are left as {"$ref": "<Name>", "unresolved": true}.

Returns the spec's top-level required list, the top-level field names, and both schemas. Remember the engine's rule: a field the product needs that is omitted is an error, never a default.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
depthNo
endpointYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.4

TDQS

A3.8/5.0
Behavior4/5

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

No annotations, so the description carries the burden, and it does disclose unusual behavior: depth controls $ref inlining, deeper refs are marked with '{"$ref": ..., "unresolved": true}', unknown endpoints error with a valid-list, and the engine rule that omitted-but-needed fields are an error, not a default. Missing only auth/rate-limit notes.

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 purpose sentence, then Args and Returns sections. The docstring layout is slightly awkward inside a description field and the 'Remember the engine's rule...' line reads as an addendum, but every sentence carries information.

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?

An output schema exists, so return-value detail is not required, yet the description still summarizes what comes back (top-level required list, field names, both schemas). Combined with error behavior and the no-implicit-defaults rule, an agent has enough to call this correctly.

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 description coverage is 0%, so the description must compensate, and it largely does: endpoint is characterized as one of 24 POST paths with leading slash optional, and depth is given a range (0..8), a default (3), and concrete behavior for values that exceed inlining. It stops short of enumerating or pointing at list_endpoints for valid names.

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?

Names a specific verb+resource: fetch request/response JSON schema for one engine endpoint. An agent can distinguish it from engine_request (which calls the endpoint) and list_endpoints (which enumerates names), though the description never states those distinctions explicitly.

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?

Gives usable context: 'one of the 24 POST paths' and the failure mode for unknown names (an error listing valid endpoints). But it never says when to reach for this versus list_endpoints, engine_request, or get_example, leaving routing to inference.

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