gapup-mcp

comp_benchmark_geo_delta

Read-onlyIdempotent

Compares local compensation benchmarks against HQ standards for CHROs, adjusting for cost-of-living and tax differentials. Inputs include job role, local and HQ locations, and salary range. Outputs include adjusted benchmark delta, cost-of-living multiplier, and tax impact. Keywords: compensation benchmark, geographic pay equity, cost-of-living adjustment, tax differential analysis.

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.
`jobRole`	Yes	Standardized job role (e.g., 'Software Engineer III')
`currency`	No	ISO 4217 currency code (e.g., 'USD')
`baseSalary`	No	Current base salary in local currency
`hqLocation`	Yes	HQ location (ISO 3166-2 code or city, country)
`localLocation`	Yes	Local work location (ISO 3166-2 code or city, country)

Output Schema

TableJSON Schema

Name	Required	Description
`status`	Yes
`sources`	No
`warnings`	No
`taxImpact`	No	Estimated tax differential percentage
`adjustedSalary`	No	Salary adjusted for cost-of-living and taxes
`benchmarkDelta`	No	Percentage difference between local and HQ benchmark
`confidenceScore`	No	0-1 confidence in data quality
`costOfLivingMultiplier`	No	Local cost-of-living index relative to HQ

TDQS

A4/5.0

Behavior4/5

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

Annotations already indicate readOnly, openWorld, and idempotent hints. The description adds valuable behavioral context: the tool adjusts for cost-of-living and tax differentials and outputs specific metrics like adjusted benchmark delta, cost-of-living multiplier, and tax impact. 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 concise, front-loaded with the main action, and structured in three sentences. Every sentence adds value, including a list of keywords. No wasted words.

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 presence of an output schema, the description adequately covers inputs and outputs. It mentions the main outputs (adjusted benchmark delta, cost-of-living multiplier, tax impact). Could be slightly improved by noting when not to use it, but overall 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?

Schema coverage is 100%, and all parameters have descriptions. The description reiterates key inputs (job role, locations, salary range) but does not add significant new meaning beyond the schema definitions. Baseline score of 3 is appropriate.

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's function: comparing local compensation benchmarks against HQ standards for CHROs, with specific inputs and outputs. It distinguishes itself from sibling tools like executive_comp_peer_benchmark and global_salary_inflation_adjuster by focusing on geographic pay equity.

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 geographic compensation benchmarking but does not explicitly provide when-to-use vs when-not-to-use or alternative tools. It includes keywords that help, but lacks explicit guidance.

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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