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cyntrica

Gov Data MCP

by cyntrica

bea_personal_income

Read-only

Get personal income by state from BEA Regional data. Choose summary, per capita, or component tables (wages, dividends, transfers) and filter by year, geography, and line code.

Instructions

Get personal income data by state from BEA Regional dataset.

Table options:

  • SAINC1: Personal income summary (income, population, per capita) — default

  • SAINC3: Per capita personal income only

  • SAINC4: Personal income by major component (wages, dividends, transfers)

LineCode for SAINC1: 1=personal income, 2=population, 3=per capita income (default) LineCode for SAINC4: 1=total, 50=wages, 45=dividends/interest/rent, 47=transfer receipts

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoYear(s): 'LAST5' (default), 'ALL', or comma-separated years
geo_fipsNo'STATE' (all states, default), or state FIPS + '000'. 'COUNTY' for all counties, 'MSA' for all metro areas.
line_codeNoSAINC1: '3' (per capita, default), '1' (personal income), '2' (population). SAINC4: '50' (wages), '45' (property income), '47' (transfers)
table_nameNo'SAINC1' (personal income summary, default), 'SAINC3' (per capita only), 'SAINC4' (by component)
Behavior3/5

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

Annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds useful context about table options and line code meanings, but it does not disclose other behavioral aspects like rate limits, default response format, or handling of invalid parameter combinations. This aligns with the baseline for annotation-supported tools.

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 efficiently structured with a brief opening sentence followed by clear bullet-pointed table options and line code lists. Every line earns its place, with no fluff or repetition.

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 simplicity of the tool (4 params, all optional, no enums, no output schema), the description covers the key choices and defaults needed to invoke the tool correctly. It does not mention return format or potential edge cases, but those are not critical given the schema's richness.

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?

The input schema already provides 100% coverage with descriptions for all four parameters. The tool description repeats and reformats line code mappings but does not add significant new meaning beyond the schema. Thus a 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 opens with a specific verb-resource pair: 'Get personal income data by state from BEA Regional dataset.' It further lists table options (SAINC1, SAINC3, SAINC4) and line codes, clearly distinguishing this from sibling GDP tools like bea_gdp_by_state. The purpose is unambiguous and well-scoped.

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 implies when to use the tool (when personal income by state is needed) and provides table choices to select the right data subset. However, it does not explicitly mention alternatives or when not to use this tool (e.g., for GDP data use bea_gdp_by_state), 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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