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Spanish Top Tech Companies

Salary statistics

salary_stats
Read-only

What a job pays in Spain across companies: median base, the middle half (p25-p75), median total compensation (base plus bonus and stock) and the share of people at 60,000 EUR base or more, in euros per year, with how many people the figures come from. Every argument is optional and narrows the selection (role, region, years of experience); none gives all of Spain. A figure comes only from 5 or more people: a selection with fewer returns no result and wider_selections, the same selection without years and then without region, to quote instead. For one company use company_salaries, and to rank companies use top_companies. Call list_filters when unsure how a role or region is named.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "salary_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault
roleNoRole slug or name, e.g. software-engineer, Data Scientist, product-manager. Omit for all roles.
yearsNoYears of experience, as a number such as 6 or a bucket such as 5-7. Omit for any experience.
regionNoSpanish region code or name, e.g. MD, Madrid, CT, Cataluña. Omit, or pass spain or remote, for all of Spain.
contextNoWhy are you calling this tool? Describe the goal in general terms, and refer to people by role ("a candidate") rather than by name. Leave out personal and identifying information.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Beyond the readOnly/openWorld annotations, it discloses a minimum-sample rule (figures only from 5+ people), the exact failure behavior (no result plus wider_selections returned), and a recommended fallback of quoting the same selection without years then without region. That is substantive operational context an agent could not infer from annotations or schema.

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 scoping and fallback rules are front-loaded and every sentence carries information, but the text is dense with long comma-chained clauses (the metric list and the wider_selections sentence) and would read faster split into shorter units. No filler or redundancy.

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?

There is no output schema, so the description carries the return-value burden itself and does so by enumerating the metrics and the sample-size accompaniment. Combined with the small-sample fallback and the cross-references to three siblings, nothing material is missing for a four-optional-parameter read tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so per-parameter meanings are already documented and the baseline is 3. The description adds value the schema does not: the combination behavior of the filters (all optional, all narrowing, none spanning all of Spain) and the interaction with the sample-size floor. It says nothing extra about the context parameter's framing, which keeps it from a 5.

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 opens with a specific verb-plus-resource statement: pay figures in Spain, naming the four metrics returned (median base, p25-p75, median total comp, share at 60k+) plus sample size. It also explicitly separates itself from company_salaries (one company) and top_companies (ranking), so an agent can choose without inspecting either schema.

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 states that every argument is optional and narrows the selection, warns that none covers all of Spain, names the alternative tools for the single-company and ranking cases, and routes the agent to list_filters when role or region naming is uncertain. Both when-to-use and when-to-use-something-else are covered.

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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