gapup-mcp

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

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.
`region`	Yes	Geographic focus (US state code or 'US' for national, e.g., 'CA', 'US')
`skills`	Yes	List of AI-related skills to forecast (e.g., ['machine learning', 'computer vision'])
`horizon_months`	No	Forecast horizon in months (3-24, default 12)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`forecast`	No
`metadata`	No
`warnings`	No

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds valuable context: it uses patent and job data, returns 12-month projections with confidence scores, and lists specific outputs. This goes beyond what annotations provide, though no detailed behavioral caveats are given.

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 a single three-sentence paragraph that front-loads the purpose and audience. It is reasonably concise but includes some redundancy (e.g., 'Inputs:' and 'Outputs:' lists reiterate schema). Still, it is well-structured and avoids fluff.

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

Completeness5/5

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

Given the tool has a complete input schema (100% coverage) and an output schema, the description provides sufficient context: audience, data sources, output types, and use case. It fully explains what the tool does and what the output contains.

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 clear descriptions. The description mentions the parameters generically ('target AI skills', 'geographic focus') but does not add deeper semantics beyond what the schema already provides, so baseline score of 3 is appropriate.

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 'forecasts AI skill demand trends for CHROs' using specific data sources (USPTO PatFT and BLS API). It distinguishes itself from sibling tools like 'job_postings_intelligence' and 'patent_landscape' by combining both datasets and focusing on AI skills, making its purpose unique.

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 explicitly states the target audience (CHROs) and use case (prioritizing workforce upskilling). However, it does not provide guidance on when not to use this tool or mention alternatives among the many sibling 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

C2.5/5.0

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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