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Startup Jobs

by io.github.killbridge
OverviewDiscussionsAdmin

Salary benchmarks

salary_benchmarks
Read-onlyIdempotent

Salary benchmarks (yearly, USD) computed from startup.jobs listings that disclose compensation, optionally filtered by role and/or country. The free tier returns an aggregate over the last 12 months; full access unlocks the monthly time series and company-level benchmarks.

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_benchmarks"
  }
}

Input Schema

NameRequiredDescriptionDefault
roleNoRole slug from list_roles (specific roles only, not top-level categories)
monthsNoHow many months of history (default 12, full access only)
companyNoCompany slug (full access only)
countryNoTwo-letter ISO country code

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

The description adds context beyond the readOnly/idempotent annotations by specifying the data source, the free tier 12-month aggregate, and full access unlocking time series and company-level benchmarks. No contradictions with annotations.

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?

Two concise, well-structured sentences cover purpose, source, filters, and tier differences with no redundant text, making it highly scannable for an agent.

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?

For a read-only benchmark tool with no required parameters, the description covers essential aspects: data source, units, filters, and access tiers. It doesn't detail return format, but annotations and schema compensate adequately.

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?

The schema already documents all 4 parameters with descriptions (100% coverage), but the description adds meaning by stating the output is yearly USD and clarifying the free tier vs full access constraints for months/company parameters, which goes beyond the schema.

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 computes yearly USD salary benchmarks from startup.jobs listings, with optional filters by role and/or country. This makes it distinct from siblings like job_trends and search_jobs, which focus on other aspects.

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 conveys when to use the tool for compensation benchmarks and clarifies the free vs full access tiers, providing clear context. However, it does not explicitly mention alternatives or exclusion cases, so it stops short of full 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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