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gapup-mcp

by io.github.getgapup

lnd_ai_skill_forecast

Read-onlyIdempotent

Forecasts AI skill demand trends for CHROs by analyzing patent filings (USPTO PatFT) and job postings (BLS API). Returns 12-month skill demand projections with confidence scores, helping HR leaders prioritize workforce upskilling. Inputs: target AI skills (e.g., 'machine learning', 'NLP'), geographic focus (US state/country), and forecast horizon. Outputs include skill growth rates, patent filing trends, and job posting volumes. Keywords: AI workforce planning, skill gap analysis, talent strategy, patent trends, labor market data.

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.
regionYesGeographic focus (US state code or 'US' for national, e.g., 'CA', 'US')
skillsYesList of AI-related skills to forecast (e.g., ['machine learning', 'computer vision'])
horizon_monthsNoForecast horizon in months (3-24, default 12)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesNo
forecastNo
metadataNo
warningsNo

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, and the description adds behavioral context: data sources (USPTO PatFT, BLS API), output type (12-month projections with confidence scores), and input constraints (skills list, region, horizon). This goes beyond annotations without contradicting them.

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?

Description is a single structured paragraph with front-loaded purpose, then Inputs/Outputs sections and keywords. It is longer than necessary but each segment adds context; no filler. Could be tightened by removing keyword list.

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 output schema exists and annotations are rich, the description covers data sources, inputs, outputs, and target users. It does not mention async polling behavior, but that is documented in the async parameter description. Overall, complete for the tool's complexity.

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 covers 100% of parameters with descriptions; description repeats inputs ('target AI skills', 'geographic focus', 'forecast horizon') with an example ('machine learning', 'NLP') but adds no significant meaning beyond schema. Baseline 3 applies given high schema coverage.

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 uses specific verb 'Forecasts' with clear resource 'AI skill demand trends' for CHROs, and distinguishes from siblings like job_postings_intelligence and patent_landscape by combining USPTO PatFT and BLS API data sources.

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: 'helping HR leaders prioritize workforce upskilling' and keywords like 'AI workforce planning, skill gap analysis, talent strategy' indicate intended use. However, it does not explicitly state when to prefer this over related siblings such as lnd_skill_taxonomy_builder or job_postings_intelligence.

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

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.

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