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fenestra-mcp

fenestra-mcp

by richer-richard
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Rust
Local

describe_schema

Returns the formal JSON Schema for fenestra descriptions to validate input grammar and enable autocomplete before rendering.

Instructions

Return the formal JSON Schema for a fenestra/1 description — a machine-checkable input grammar to validate or autocomplete against before rendering, the structured complement to describe_vocabulary's prose grammar. Structural only (shape, required/optional fields, unions); still call validate for semantic checks like colour roles, numeric ranges, and enum-like fields.

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": "describe_schema"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

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

Without annotations, the description fully discloses the tool's behavior: it is structural only (shape, fields, unions) and does no semantic validation. It explicitly directs users to validate for semantic checks, leaving no hidden side effects.

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

Conciseness5/5

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

The description is two sentences with zero wasted words. The first sentence defines purpose and relationship; the second sentence clarifies limitations and alternative. Perfectly front-loaded.

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?

Given no output schema and simple inputs, the description fully explains what the tool returns (formal JSON Schema) and what it does not do (semantic checks). Comparisons with describe_vocabulary and validate complete the context.

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 has 0 parameters, so the baseline is 4. The description does not need to add parameter details, and the context signals confirm 100% schema coverage.

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 clearly states the tool returns the formal JSON Schema for a fenestra/1 description, distinguishing it from the related describe_vocabulary tool by calling itself a 'structured complement' to its 'prose grammar'. The verb 'return' and specific resource are explicit.

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?

The description tells when to use the tool (before rendering for structural validation/autocomplete) and when not to use it (for semantic checks, defer to validate). It implicitly contrasts with describe_vocabulary but does not explicitly list all sibling tools.

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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