mcp-livedatapro

getRegionalSalaryBenchmarks

Verified base salary, bonus, and equity compensation percentiles (P25, P50, P75, P90) by job role, seniority, and city. Use when benchmarking market-wide compensation percentiles (P25, P50, P75, P90 total comp, base salary, bonus, and equity RSU in USD) by standardized job title, city, and seniority level. Do not use for listing active job openings at a single company (use getCorporateJobPostings).

Input Schema

Name	Required	Description	Default
`job_role`	Yes	Standardized job title (e.g., 'Machine Learning Engineer', 'Product Manager').
`seniority`	No	Career experience tier (defaults to 'senior').	senior
`region_or_city`	Yes	Target metropolitan city or country name (e.g., 'London', 'San Francisco', 'Zurich').

Schema Changelog

Changes observed during successful MCP inspections.

Changed8 schema fields changedOct 24, 2026
- addedInput schema / additionalProperties
```
Added value: +false
```
- addedInput schema / properties / job_role / examples
```
Added value: +[
+  "Machine Learning Engineer"
+]
```
- addedInput schema / properties / job_role / minLength
```
Added value: +2
```
- addedInput schema / properties / region_or_city / examples
```
Added value: +[
+  "London"
+]
```
- addedInput schema / properties / region_or_city / minLength
```
Added value: +2
```
- addedInput schema / properties / seniority / default
```
Added value: +"senior"
```
- addedInput schema / properties / seniority / enum
```
Added value: +[
+  "junior",
+  "mid",
+  "senior",
+  "staff_principal",
+  "executive"
+]
```
- addedInput schema / properties / seniority / examples
```
Added value: +[
+  "senior"
+]
```

Changed3 schema fields changedOct 24, 2026

changedInput schema / properties / job_role / description

Previous value: -"Standardized job title (e.g., Machine Learning Engineer, Product Manager, Financial Analyst)"New value: +"Standardized job title (e.g., 'Machine Learning Engineer', 'Product Manager')."

changedInput schema / properties / region_or_city / description

Previous value: -"City or country (e.g., London, San Francisco, Berlin, Budapest)"New value: +"Target metropolitan city or country name (e.g., 'London', 'San Francisco', 'Zurich')."

changedInput schema / properties / seniority / description

Previous value: -"Experience level"New value: +"Career experience tier (defaults to 'senior')."

First observedOct 21, 2026

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries the behavioral burden and does well by disclosing that the data is market-wide, verified, percentile-based, and denominated in USD. It does not disclose freshness, source coverage, or update cadence, which keeps it from being fully transparent about data provenance.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded and appropriately sized, with clear use and exclusion guidance. It repeats the same percentile and compensation metric list across the first two sentences, which is slightly redundant but not harmful.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple three-parameter read lookup with no output schema, the description explains what is returned, the aggregation dimensions, the currency, and the sibling exclusion. That is sufficient for an agent to invoke it correctly.

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 100%, so the parameters are already well documented. The description reinforces that job titles are standardized and that seniority and city matter, but it adds little beyond the schema and does not mention that region_or_city can also be a country.

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 states a specific data resource and scope: verified compensation percentiles by role, seniority, and city, with named metrics such as base salary, bonus, equity, and total comp. It also explicitly distinguishes this tool from getCorporateJobPostings, so an agent can tell what it is and is not for.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives an explicit use case: benchmarking market-wide compensation percentiles by standardized title, city, and seniority. It also names an exclusion and routes the agent to the correct sibling: do not use for active job openings at a single company, use getCorporateJobPostings.

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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