gapup-mcp

talent_litigation_exposure

Read-onlyIdempotent

Estimates litigation exposure risk for CHROs by analyzing past employee lawsuits, settlement amounts, and industry benchmarks. Inputs include company location, industry code, and employee count range. Returns exposure score, average settlement amounts, lawsuit frequency trends, and risk factors. Ideal for legal risk assessment, HR strategy planning, and board-level reporting. 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_code`	Yes	NAICS industry code (e.g., '541511' for IT services)
`employee_count`	No	Current number of employees
`lookback_years`	No	Number of years to analyze
`company_location`	Yes	State or region where company operates (e.g., 'CA', 'New York')

Output Schema

TableJSON Schema

Name	Required	Description
`status`	Yes
`sources`	Yes
`warnings`	Yes
`avg_settlement`	No	Average settlement amount in USD
`exposure_score`	Yes	Normalized risk score (0-100)
`historical_trend`	No
`top_risk_factors`	No
`lawsuit_frequency`	No	Lawsuits per 1000 employees per year
`industry_benchmark`	No	Industry average exposure score

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and openWorldHint=true. The description adds value by noting the async option to avoid timeout, indicating potential slowness. No contradictions.

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?

The description is brief and front-loaded: first sentence states purpose, then inputs, outputs, use cases, and a practical tip. No wasted words.

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 complexity (5 parameters, output schema, annotations), the description fully covers purpose, inputs, outputs, usage guidance, and a timeout avoidance tip. It is complete for effective tool selection.

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%, so baseline is 3. The description mentions key inputs but does not elaborate on all parameters (e.g., lookback_years). The async tip adds some value 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 uses a specific verb (Estimates) and resource (litigation exposure risk), clearly distinguishing it from sibling tools like talent_contract_risk_mapper. It states the tool's function, inputs, outputs, and ideal use cases.

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?

The description provides context by listing ideal use cases (legal risk assessment, HR strategy planning, board-level reporting) but does not explicitly state when not to use it or mention alternatives among 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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