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Generative AI Maturity Framework

get_framework_spec

Return the full canonical machine-readable framework spec — six dimensions, six levels, scoring scale, assessment types, endpoints. CC BY 4.0.

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior3/5

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

No annotations exist, so the description carries the burden, and it does disclose the payload scope plus the CC BY 4.0 licensing/attribution obligation, which is genuinely useful context. It omits return format, payload size, and whether the response is a single document or sectioned, which matters for a 'full spec' call.

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?

One sentence with the verb and full content inventory front-loaded, followed by a short licensing clause. Every element earns its place; nothing is padded.

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?

With no output schema, the description takes on the job of describing return values, and it does so by enumerating the sections included (dimensions, levels, scoring scale, assessment types, endpoints). It stops short of stating the serialization format or approximate size, which is the only meaningful gap for a zero-param getter.

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 there is nothing for the description to disambiguate; baseline applies. The description correctly presents it as an argument-free retrieval call.

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 (Return) and resource (the full canonical framework spec) and enumerates the payload contents: dimensions, levels, scoring scale, assessment types, endpoints. The word 'full' implicitly distinguishes it from granular siblings like get_dimension and get_maturity_level, but no sibling is named directly.

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?

Usage is only implied: an agent can infer this is the bootstrap/overview call to make before drilling into get_dimension or get_maturity_level, but the description never says when to use it or when to prefer the narrower siblings. No exclusions or prerequisites are given.

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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