gapup-mcp

lnd_skill_taxonomy_builder

Read-onlyIdempotent

Generates a dynamic skill taxonomy for CHROs by cross-referencing patent filings (USPTO), job postings (BLS), and learning & development data (OECD). Inputs include industry codes, job roles, or skill clusters; outputs structured skill hierarchies with demand trends and competency gaps. Essential for workforce transformation, talent pipeline optimization, and future-proofing organizational capabilities. — pass async:true REQUIRED to avoid x402 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.
`jobRole`	No	Target job role or occupation (e.g., 'Data Scientist')
`industry`	Yes	NAICS industry code or sector name (e.g., '541511' for IT services)
`timeRange`	No	Time range for trend analysis
`skillCluster`	No	Optional skill cluster to focus taxonomy (e.g., 'AI/ML')

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`warnings`	No
`skillTaxonomy`	No
`industryTrends`	No

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already provide readOnlyHint, openWorldHint, and idempotentHint. The description adds behavioral context by detailing data sources and the mandatory async flag. It does not contradict 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 concise with two sentences and a key usage note. It front-loads the primary action. Some marketing language is present but not excessive.

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 and annotations, the description covers the tool's purpose, data sources, input/output types, and the critical async requirement. It does not explain polling but the output schema likely provides return structure context.

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 coverage is 100% with adequate descriptions for each parameter. The description summarizes input types but adds no new parameter-level details beyond the schema.

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 dynamic skill taxonomy for CHROs using specific data sources (USPTO, BLS, OECD), and specifies inputs (industry codes, job roles, skill clusters) and outputs (structured skill hierarchies with demand trends and competency gaps). This differentiates it from sibling tools like lnd_ai_skill_forecast.

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 acceptable inputs and emphasizes the async requirement to avoid timeouts. It also positions the tool for workforce transformation. However, it does not explicitly state when not to use it or name alternative tools for different use cases.

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.

Resources

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