gapup-mcp

talent_poaching_risk

Read-onlyIdempotent

Analyzes employee poaching risk for CHROs by evaluating LinkedIn profile activity (job searches, profile views) and comparing compensation against BLS benchmarks. Returns a ranked list of high-risk employees with risk scores and suggested retention actions. Ideal for proactive talent retention strategies. Keywords: employee retention, poaching risk, compensation benchmark, LinkedIn activity, CHRO analytics.

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.
`location`	No	Geographic location filter (e.g., 'San Francisco, CA')
`department`	Yes	Department filter (e.g., 'Engineering', 'Sales')
`min_tenure_months`	No	Minimum tenure in months to include in analysis
`benchmark_job_title`	No	Specific job title for compensation benchmarking

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`warnings`	No
`risk_assessment`	No
`department_avg_risk`	No
`benchmark_comparison`	No

TDQS

A4.2/5.0

Behavior4/5

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

Annotations (readOnlyHint, openWorldHint, idempotentHint) already indicate the tool is safe, idempotent, and uses external data. The description adds valuable context about analyzing LinkedIn activity and BLS benchmarks, enhancing transparency without contradicting annotations.

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 two concise sentences plus keywords, front-loading the core purpose and output. Every sentence adds value with no redundancy.

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?

The tool has an output schema and moderate complexity. The description covers what it returns (ranked list with scores and actions) and its data sources, leaving minimal gaps. It could note prerequisites like LinkedIn data access, but overall it is sufficiently complete.

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?

All 5 parameters have descriptions in the input schema (100% coverage), so the schema already documents parameter meanings. The tool description does not add further semantic detail beyond what the schema provides.

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 explicitly states the tool analyzes employee poaching risk using LinkedIn profile activity and compensation benchmarks, returning a ranked list with risk scores and retention actions. It distinguishes itself from similar talent tools like talent_intelligence by its specific focus on poaching risk and proactive retention strategies.

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 indicates it is ideal for proactive talent retention strategies and targets CHROs, providing clear usage context. However, it does not explicitly state when not to use this tool or mention alternative tools for different talent scenarios.

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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