convert_geometry1:1 wrapper for /api/geometry/. Convert geometry between ESRI, WKT, GeoJSON, GPolyline, and EncodedGPolyline.
| Name | Required | Description | Default |
|---|---|---|---|
| to | Yes | ||
| from | Yes | ||
| srid | No | ||
| geometry | Yes | Source geometry as a string. JSON inputs should be JSON.stringify'd. |
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 clear. The description adds that it is a '1:1 wrapper' implying direct mapping with no hidden effects, but does not elaborate on authorization or rate limits. It adds moderate context beyond 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 a single, front-loaded sentence that immediately states the tool's nature (API wrapper) and then lists the convertible formats. Every part is relevant, and there is no extraneous text.
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 straightforward conversion tool with four parameters, the description, combined with the schema and annotations, provides sufficient guidance. However, it omits explanation of the optional 'srid' parameter and the exact string format for geometry input, which are partially covered by the schema description.
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?
The description only mentions the conversion between formats, which hints at 'to' and 'from' parameters, but does not explain the optional 'srid' parameter or the requirement that 'geometry' be a stringified JSON. With only 25% schema description coverage, the description fails to add meaningful detail beyond what the schema already 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 clearly states it is a 1:1 wrapper for converting geometry between five specified formats. It uses the specific verb 'convert' and resource 'geometry', and the listed formats distinguish it from sibling tools which are about data retrieval.
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 implies when to use the tool (when geometry conversion is needed) but does not explicitly state when not to use it or suggest alternatives. However, the context is clear and the tool's purpose is distinct from siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target distinct data types (assessor, permits, land use, sewer, geometry, etc.), but get_portlandmaps_detail overlaps conceptually with the specific search tools as a generic detail fetcher. This could cause confusion about which tool to use for a particular record.
The vast majority follow a verb_noun snake_case pattern. However, two tools (get_portlandmaps_detail, suggest_portlandmaps_address) redundantly include the server name, breaking the otherwise consistent naming scheme.
11 tools cover a broad range of Portland city data and GIS operations. The count is well-scoped for the domain—neither too sparse nor excessive.
The server covers major data categories like assessor, permits, land use, sewer, geometry, and spatial queries. Potential gaps like explicit zoning or code lookups might exist, but the set is largely comprehensive for typical PortlandMaps queries.