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swagger-doc-explorer-mcp

by anuoua

List Component Schemas

swagger_list_schemas
Read-onlyIdempotent

List all data models (schemas) defined in a loaded OpenAPI spec, showing their types and property counts to get an overview of the API's structure.

Instructions

List all component schemas (data models) defined in the loaded OpenAPI spec.

Use this tool to get an overview of all data models used by the API, including their types and number of properties.

Args:

  • spec_name (string): Name of the previously loaded spec

  • limit (number): Maximum results to return (default: 50)

  • offset (number): Number of results to skip (default: 0)

Returns: { "total": number, // Total number of schemas "count": number, // Number of results in this response "offset": number, // Current pagination offset "schemas": [{ "name", "type", "description", "properties" }], "has_more": boolean, "next_offset": number }

Examples:

  • Use when: "What data models are defined?" -> params with spec_name=""

  • Use when: "Show me all schemas" -> params with spec_name=""

Error Handling:

  • Returns error if the spec name has not been loaded yet

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum results to return (default: 50)
offsetNoNumber of results to skip for pagination (default: 0)
spec_nameYesName of the previously loaded OpenAPI/Swagger spec (use swagger_list_loaded to see available names)
Behavior5/5

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

Annotations already declare readOnlyHint and idempotentHint, and the description adds valuable behavioral details: the return payload structure, pagination semantics (limit, offset, has_more, next_offset), and error handling if the spec name is not loaded. This goes beyond annotations.

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?

The description is well-structured with sections for Args, Returns, Examples, and Error Handling. It is slightly redundant with the schema for parameter descriptions, but the additional return format and examples justify the length.

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?

The tool is simple, but the description covers the purpose, usage, parameters, return format, pagination, and error handling. Combined with strong annotations, this provides complete contextual guidance for an agent.

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

Parameters3/5

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

The input schema covers all three parameters with complete descriptions (100% coverage), so the description adds little beyond that. The 'Args' section restates schema information, and the examples provide usage context but not deeper parameter semantics.

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

Purpose5/5

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

The description uses the specific verb 'List' with the resource 'all component schemas (data models) defined in the loaded OpenAPI spec', clearly distinguishing it from siblings like swagger_get_schema. The scope is unambiguous.

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?

It provides clear when-to-use examples ('What data models are defined?') and states the tool is for getting an overview of all data models. However, it does not explicitly mention alternatives or when not to use it, so it lacks explicit exclusions.

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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