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mcp-knowledge

by io.github.getgapup

vertical_ai_agent_governance

Read-onlyIdempotent

Generates a comprehensive vertical AI agent workforce integration plan for CHROs, including governance frameworks, human-AI collaboration metrics, and upskilling recommendations. Inputs: industry vertical, workforce size, and current AI adoption level. Outputs: role-specific AI integration roadmaps, skill gap analysis, and performance benchmarks. Uses O*NET skill taxonomies and Gartner AI adoption trends. For best results with large datasets, pass async:true to avoid timeout.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
industryYes
target_rolesNo
workforce_sizeYes
ai_adoption_levelNo
include_benchmarksNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesNo
warningsNo
skill_gap_analysisNo
integration_roadmapNo
collaboration_metricsNo
governance_recommendationsNo

TDQS

A4/5.0
Behavior4/5

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

Annotations indicate readOnly, openWorld, and idempotent hints. The description adds context about potential timeout and advises async usage, plus mentions data sources (O*NET, Gartner). No contradiction with 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, starting with the main purpose, then listing inputs, outputs, and data sources. It is concise but could perhaps be slightly tighter.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 6 parameters and an output schema, the description covers main inputs and outputs but not all parameters. With output schema present, return values need not be detailed, but parameter list is not fully explained.

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 only 17%. The description mentions industry, workforce_size, and ai_adoption_level, but omits async, target_roles, and include_benchmarks. Partial compensation but incomplete.

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 generates a 'comprehensive vertical AI agent workforce integration plan for CHROs', specifying a specific verb and resource. It distinguishes itself from siblings like 'ai_governance_full_report_async' by being vertical-specific.

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 lists required inputs and outputs, and advises using async for large datasets to avoid timeout. However, it does not explicitly say when to use this tool versus alternatives among sibling AI governance 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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TDQS

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

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