gapup-mcp

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

Name	Required	Description
`async`	No	If 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.
`industry`	Yes
`target_roles`	No
`workforce_size`	Yes
`ai_adoption_level`	No
`include_benchmarks`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`warnings`	No
`skill_gap_analysis`	No
`integration_roadmap`	No
`collaboration_metrics`	No
`governance_recommendations`	No

TDQS

A3.7/5.0

Behavior4/5

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

Annotations (readOnlyHint, openWorldHint, idempotentHint) already indicate safety and idempotence. Description adds specific outputs, data sources (O*NET, Gartner), and async behavior, providing valuable context beyond structured fields.

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?

Concise single paragraph (~60 words) with clear front-loading of purpose. Could benefit from structure like bullet points, but remains efficient and readable.

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?

Given the presence of an output schema (mentioned in context signals), the description covers purpose, key inputs, outputs, and a usage hint. Lacks error handling or corner cases, but is reasonably complete for a 6-parameter tool with output schema.

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 low (17%). Description mentions 'industry vertical, workforce size, and current AI adoption level' but omits target_roles, include_benchmarks, and async (though async is noted in usage tip). Partially compensates but not fully for low coverage.

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?

Clearly states verb 'generates' and resource 'vertical AI agent workforce integration plan' for CHROs, listing included components. Distinguishes from sibling tools by targeting CHROs and workforce planning, but lacks explicit differentiation from other AI governance tools.

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?

Provides context for when to use (with industry, workforce size, AI adoption inputs) and an async optimization tip for large datasets. Does not specify when not to use or list alternative sibling tools, leaving some ambiguity.

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

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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