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

Annotations already mark the tool read-only and idempotent, and the description adds valuable behavioral caveats: published 90% margins of error, missing/suppressed values are not zero, resident vs. workplace populations must not be combined, and the data is place-level rather than company-level. No contradiction 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.

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

The description is front-loaded with the data inventory, followed by important caveats. Each sentence contributes useful information, though the set of disclaimers makes it slightly denser than strictly necessary.

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

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

With no output schema, the description compensates by enumerating data sources, variables, margins of error, national reference rows, and caveats. It does not specify the exact response shape, but for a single-parameter read-only lookup it is nearly complete.

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

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

Schema coverage is 100% and the schema already documents the FIPS code/name format, examples, and ambiguous-name candidate-list behavior. The description mentions lookup by FIPS/name but adds no parameter-level meaning beyond the schema, so it stays at the baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description is highly specific: it names exact datasets (Census ACS, BLS QCEW), the variables covered, the geographic scope (one US county), and the lookup key format. It also distinguishes itself from company-level tools by explicitly stating it describes a place, not company exposure.

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

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

The description gives clear use context: county-level research/education, and explicit when-not guidance: do not combine resident and workplace populations, and do not use this for company operating or revenue exposure. It stops short of a 5 because it does not name sibling alternatives such as get_economic_context or get_company_research.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.