interpolation_interpolate

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 the tool's behavior: it performs spatial interpolation using inverse distance weighting, outputs GeoJSON FeatureCollections, handles specific input formats (JSON strings), and raises exceptions for failures. It also notes dependencies (Turf.js, Node.js) and assumptions (closer points have more influence). However, it lacks details on performance, rate limits, or specific error conditions beyond generic exceptions.

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), making it easy to navigate. It is appropriately sized for a complex tool with 3 parameters, though some sections (like the detailed example) are lengthy. Every sentence adds value, but it could be slightly more front-loaded by moving key usage notes earlier.

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 (spatial interpolation with 3 parameters), no annotations, and an output schema present, the description is highly complete. It covers purpose, parameters, return values, errors, examples, and notes on dependencies and assumptions. The output schema handles return format details, so the description focuses on operational context, making it sufficient for effective use.

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 description coverage is 0%, so the description must fully compensate. It provides comprehensive parameter semantics: 'points' is explained as a GeoJSON FeatureCollection with examples, 'cell_size' as grid cell distance with units, and 'options' as a JSON string with detailed optional fields (gridType, property, units, weight, bbox), including defaults and examples. This adds significant meaning beyond the bare schema.

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: '使用反距离权重法进行空间插值' (performs spatial interpolation using inverse distance weighting). It specifies the method (inverse distance weighting), the resource (known points' attribute values), and the output (estimated values on a grid). This distinguishes it from sibling interpolation tools like 'interpolation_isobands', 'interpolation_isolines', and 'interpolation_tin' by explicitly naming the algorithm.

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 context through the method explanation and parameter details, such as when to use certain grid types or properties. However, it does not explicitly state when to choose this tool over alternatives like 'interpolation_tin' or 'interpolation_planepoint', nor does it provide prerequisites or exclusions. The guidance is present but not direct.

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