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mcp-knowledge

by io.github.getgapup

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
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.
jobRoleNoTarget job role or occupation (e.g., 'Data Scientist')
industryYesNAICS industry code or sector name (e.g., '541511' for IT services)
timeRangeNoTime range for trend analysis
skillClusterNoOptional skill cluster to focus taxonomy (e.g., 'AI/ML')

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesNo
warningsNo
skillTaxonomyNo
industryTrendsNo

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. Description adds that the tool is computationally intensive (requires async to avoid timeout), uses multiple data sources, and outputs structured hierarchies. This goes beyond annotations without contradiction.

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?

Two sentences with no waste. First sentence covers purpose, inputs, outputs, and value. Second sentence provides critical usage instruction. Front-loaded for quick scanning.

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 complexity (multi-source dynamic taxonomy), presence of output schema, and annotations, the description sufficiently covers core functionality and a key behavioral trait (async requirement). Could expand on polling mechanism but not required since async hint is in schema.

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?

Input schema has 100% description coverage for all 5 parameters, so baseline is 3. Description adds value by grouping inputs (industry codes, job roles, skill clusters) and describing output characteristics, which aids understanding of parameter purpose beyond 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?

Description clearly states it generates a dynamic skill taxonomy for CHROs using specific data sources (USPTO, BLS, OECD), lists inputs (industry codes, job roles, skill clusters) and outputs (structured hierarchies with demand trends and competency gaps). It distinguishes from many sibling tools by its focus on taxonomy building for workforce transformation.

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?

Provides clear context (for CHROs) and an essential usage requirement: 'pass async:true REQUIRED to avoid x402 timeout.' Does not explicitly mention when not to use or name alternatives, but the async directive is actionable.

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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