mcp-knowledge

executive_comp_peer_benchmark

Read-onlyIdempotent

As a Chief Human Resources Officer (CHRO), benchmark executive compensation packages against peer companies using public SEC filings and private compensation data from Equilar and Bloomberg. Inputs include executive name, title, company ticker, and peer group criteria. Outputs structured compensation metrics (base salary, bonus, equity, total compensation) with source attribution and confidence scores.

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.
`peerGroup`	No
`fiscalYear`	No
`companyTicker`	Yes
`executiveName`	Yes
`executiveTitle`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`warnings`	No
`compensation`	No

TDQS

A3.8/5.0

Behavior3/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds context about data sources and confidence scores, but does not disclose behavioral traits beyond annotations. 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 a single concise paragraph of about 60 words, front-loaded with the role and action. Every sentence adds value with no fluff.

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 (6 params, nested object, output schema), the description provides a good high-level understanding. It covers main inputs, outputs, and data sources. However, it omits details on the async parameter and the peerGroup structure, which are explained only in the schema.

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 description coverage is only 17%. The description adds meaning for required parameters (executiveName, executiveTitle, companyTicker) by explaining they are inputs, and mentions peer group criteria. However, optional parameters like async and fiscalYear are not addressed, leaving gaps.

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 benchmarks executive compensation against peers, specifying the user role (CHRO), data sources (SEC filings, Equilar, Bloomberg), inputs, and outputs. It distinguishes from sibling tools like comp_benchmark_geo_delta and comp_plan_architect by focusing on peer benchmarking for executives.

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 implies usage for benchmarking executive compensation but does not explicitly state when to use it versus alternatives. No exclusions or conditions are provided, leaving the agent to infer context from the role and sibling list.

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.