Turf-MCP

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: the tool decomposes geometries into independent line segments, returns a GeoJSON FeatureCollection of LineString features, raises exceptions for execution failures or input errors, and depends on Turf.js and Node.js. It covers output format, error conditions, and dependencies, though it lacks details on performance or rate limits.

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 well-structured with clear sections (Args, Returns, Raises, Example, Notes) and front-loaded purpose. It is appropriately sized for a tool with one parameter and detailed requirements, though the example and notes could be slightly condensed. Every sentence adds value, with no redundant information.

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?

Given the tool's complexity (geometric decomposition), no annotations, 0% schema coverage, and the presence of an output schema, the description is highly complete. It covers purpose, input details, output format, error handling, dependencies, and provides an example. The output schema handles return values, so the description appropriately focuses on behavioral context and parameter semantics.

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 schema description coverage is 0%, so the description must fully compensate. It provides comprehensive semantics for the single parameter 'geojson': type (JSON string), supported formats (LineString, MultiLineString, Polygon, MultiPolygon), coordinate system (WGS84 with longitude first), and an example. This adds significant value beyond the basic schema, which only indicates it's a required string.

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 the tool's purpose: '将几何图形分解为线段' (decompose geometric shapes into line segments). It specifies the input (GeoJSON objects) and output (independent line segments with two vertex coordinates each), distinguishing it from siblings like 'feature_conversion_explode' or 'feature_conversion_polygon_to_line' by focusing on segment-level decomposition rather than feature conversion.

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 usage through the Args section, which lists supported GeoJSON types (LineString, MultiLineString, Polygon, MultiPolygon) and coordinate system (WGS84). However, it does not explicitly state when to use this tool versus alternatives like 'misc_line_slice' or 'feature_conversion_polygon_to_line', nor does it provide exclusions or prerequisites beyond input format requirements.

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