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Generative AI Maturity Framework

get_dimension

Get one of the six Generative AI Maturity Framework dimensions with full editorial content — definition, signals, per-level signals, common blockers, how to move up, related standards.

Input Schema

NameRequiredDescriptionDefault
idYesDimension id — one of strategy, data, usecases, talent, governance, agentic.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It does disclose the payload shape ('full editorial content' plus six named content sections), which implicitly frames this as a read-only lookup, but it says nothing about error behavior for an invalid id, response size, or whether content is static versus versioned.

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 front-loaded sentence with the resource and action up front, then the returned contents after the em dash. Nothing is redundant with the title or schema, and there is no filler.

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 must stand in for return values, and it does so by enumerating the content sections. The only remaining gap is the response envelope (single object vs. structured sections vs. rendered markdown) and error behavior for an out-of-enum id, both minor for a single-parameter read.

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?

Schema description coverage is 100% and the single parameter is an enum whose description already lists all six ids, so the schema does the heavy lifting. The description only restates the cardinality ('one of the six') and names no additional semantics such as case sensitivity or alias handling.

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?

States a specific verb and resource (get a Maturity Framework dimension) and enumerates exactly what comes back: definition, signals, per-level signals, blockers, remediation, related standards. That content inventory distinguishes it from the sibling get_maturity_level, which covers the level scale rather than a single dimension's editorial dossier.

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 infers it should call this when it needs detail on one named dimension. There is no explicit when-to-use, when-not-to-use, or pointer to a sibling (e.g. get_maturity_level for level definitions), even though several siblings overlap in topic.

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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