Pinloft: Batch Geocoder — Address to Lat/Long + Census FIPS

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds valuable behavioral context: it returns a cleaned matched address and confidence, and explains the different backends (US Census vs OpenStreetMap) and optional district enrichment. This goes beyond the annotations and is transparent.

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 sentences, but the second is long and packs multiple clauses (US Census details, include_districts, OSM). The core purpose is front-loaded in the first sentence. It is efficient but slightly dense; no wasted words, though the structure could be split for readability.

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 3 parameters and no output schema, the description covers the primary behavior (geocoding, cleaning, confidence, FIPS, districts). It doesn't describe the exact return format or error handling, but for a forward-geocoding tool this is acceptable. It gives enough for an agent to call it correctly.

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% (all three parameters have descriptions), so baseline is 3. The description adds meaning beyond the schema: it explains how the 'country' parameter affects output (US vs worldwide), what 'include_districts' adds, and provides concrete example formats. This extra context helps the agent choose parameter values effectively.

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 verb ('convert'), resource ('street addresses to latitude/longitude'), and the key outputs (cleaned address, confidence, FIPS codes, districts). It also differentiates US vs worldwide behavior, clearly distinguishing it from the sibling reverse_geocode.

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 explains what it does and that US addresses get extra data, but it never explicitly says when to use this tool versus the sibling reverse_geocode. It doesn't state when not to use it or mention alternatives, leaving the agent to infer from the name and description.

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

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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive. The description adds meaningful behavioral context by naming OpenStreetMap as the address source and the US Census geocoder as the source for FIPS and district data.

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?

A single sentence delivers the core operation first and then the US-specific enrichment. It is compact, front-loaded, and contains no redundant wording.

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 one-parameter, read-only tool with rich annotations and no output schema, the description conveys input format, cardinality, data sources, and expected enriched outputs. Minor omissions such as failure behavior are acceptable for this level of simplicity.

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 coordinates parameter is already documented with format and example. The description restates the up-to-5 point limit but adds no new parameter-level semantics beyond what the schema provides.

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 names a specific operation (convert) and resource (latitude/longitude points to nearest street address), then adds the US Census enrichment. This clearly distinguishes the tool from the sibling geocode_address by direction and output.

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?

It clearly establishes the context: this tool is for converting coordinates to addresses and adding US FIPS and district data. It does not explicitly name geocode_address or state when not to use this tool, so it stops short of a 5.

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