Query Homelessness Data

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

With no annotations, the description carries the full burden. It usefully discloses provenance (January 2024 PIT count, AHAR Part 1) and coverage limits (20 cities), but does not describe return format, how missing cities are handled, or any data freshness caveats beyond the snapshot date.

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?

Two tight sentences that front-load the data source and enumerate returned measures before adding the coverage/date constraint. Efficient, though the long metric list borders on being a data dictionary.

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?

For a simple one-parameter lookup with a fully documented schema, the description is enough to know what it returns and its source. It is still incomplete about behavior for unknown cities and the exact response shape, which is not covered by any output schema.

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%, so the single city parameter is already documented with examples. The description does not add syntax, matching, or case-sensitivity details beyond what the schema provides, making the baseline of 3 correct.

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?

States a specific source (HUD Point-in-Time), resource (homelessness counts), and scope (US cities, 20 available). It clearly distinguishes itself from thematic siblings like query_housing or query_demographics by naming the exact data source and the specific measures returned.

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?

Usage is implied by the specificity of the data covered, so an agent can tell this is the tool for homelessness figures. However, there is no explicit when-to-use/when-not guidance and no routing against similar data tools such as query_housing or query_city_data.

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