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by com.1102tools
OverviewDiscussionsAdmin

compare_metros

Read-only

Compare wages for one occupation across multiple metro areas.

Pass a list of 5-digit MSA codes (e.g., ['47900', '42660', '12580']
for DC, Seattle, Baltimore). Returns the specified wage measure for
each metro. Use list_common_metros() to find codes.

datatype: '04' (Annual Mean, default), '13' (Median), '03' (Hourly Mean).

Up to 50 metros per call.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "occ_code": "<occ_code>",
      "metro_codes": "<metro_codes>"
    },
    "name": "compare_metros"
  }
}

Input Schema

NameRequiredDescriptionDefault
yearNo
datatypeNo04
occ_codeYes
metro_codesYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint/destructiveHint=false, so safety is covered; the description adds genuinely new operational facts — a hard cap of 'Up to 50 metros per call' and the shape of the return ('the specified wage measure for each metro'). It doesn't mention what happens when a metro code is invalid or how missing metros appear in the response.

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?

Front-loads the purpose, then the input format, then the return, then the datatype lookup — a sensible reading order with no filler. The embedded code list and datatype key are dense but each earns its place; only minor tightening is possible.

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

Completeness3/5

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

Return values needn't be explained given an output schema, and the metro-limit and datatype details are solid. But the unexplained 'year' parameter is a real gap for a query tool that has a sibling detect_latest_year — an agent can't tell whether omitting year means 'latest' or 'an error'.

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 0%, so the description must carry all four parameters. It does well on metro_codes (5-digit MSA format plus a worked example) and datatype (code-to-meaning mapping with default '04'), but occ_code's expected format and the entirely undocumented 'year' parameter (including whether it defaults to latest) are left to inference.

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?

States a specific verb and resource — 'Compare wages for one occupation across multiple metro areas' — which cleanly separates it from the sibling compare_occupations (the inverse axis) and from get_wage_data. An agent knows immediately what shape of question this answers.

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?

Gives a concrete prerequisite workflow: 'Use list_common_metros() to find codes', naming the exact sibling tool needed to supply metro_codes. It doesn't state when to prefer this over get_wage_data or compare_occupations, so the routing guidance is clear but not exhaustive.

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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