gapup-mcp

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

A4/5.0

Behavior3/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, covering the safety profile. The description adds that outputs include source attribution and confidence scores, but does not disclose any other behavioral traits (e.g., data freshness, latency, or error conditions). This is adequate but not rich.

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 sentences, front-loaded with the core purpose, and includes all necessary elements (inputs, outputs, sources) without waste. Every sentence earns its place.

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 tool's complexity (6 params, nested peerGroup object), the description covers purpose, inputs, outputs, and sources. The output schema exists and handles return details. It does not explain the nested peerGroup subfields, but the overall context is sufficiently complete for a read-only benchmarking tool.

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?

Only 17% of parameters have schema descriptions, so the description must compensate. It names executiveName, executiveTitle, companyTicker, and peerGroup, but omits fiscalYear and async. The description adds meaning for key inputs but leaves gaps, so it is only partly helpful.

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 action ('benchmark executive compensation packages against peer companies') and specifies the resource (peer companies) plus data sources (SEC filings, Equilar, Bloomberg). It distinguishes itself from similar tools like comp_benchmark_geo_delta by focusing on peer benchmarking with specific data sources.

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 provides clear context for when to use the tool (for a CHRO benchmarking executive compensation against peers) and lists required inputs. It does not explicitly mention exclusions or alternative tools, so it stops short of a 5.

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

C2.4/5.0

Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.