mcp-knowledge

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

A3.8/5.0

Behavior4/5

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

Annotations already indicate read-only, idempotent, and open-world behavior. The description adds value by disclosing data sources (LinkedIn activity, BLS benchmarks) and output nature (ranked list, risk scores, suggestions). This provides useful behavioral context beyond 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 three sentences plus keywords, front-loading the main purpose. Every sentence adds value, and there is no redundant information. Highly efficient.

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 the complexity (5 parameters, output schema exists), the description covers the tool's purpose, data sources, and output. It could be more complete with usage guidelines or limitations, but it is sufficient for most agents.

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 the schema already describes all parameters. The description gives overall context but does not elaborate on individual parameters beyond what the schema provides. Thus, baseline of 3 is maintained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool analyzes employee poaching risk using LinkedIn activity and compensation data, targeting CHROs. It specifies the output: a ranked list with risk scores and retention actions. However, it does not differentiate from sibling tools like 'talent_intelligence' or 'comp_benchmark_geo_delta', so a 4 is appropriate.

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 mentions ideal use for proactive talent retention strategies, implying context. However, it lacks explicit guidance on when to use this tool versus alternatives or when not to use it. No exclusions or cross-references are provided.

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.

talent_poaching_risk

Input Schema

Output Schema

TDQS

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TDQS

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