Search UdonSharp examples to use as a code base before writing, preventing incorrect APIs and networking patterns for VRChat.
WHEN: before writing UdonSharp (prefer with get_template). HOW: use matching examples as the code BASE — adapt, do not invent APIs or networking patterns. Then validate_code.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | Find examples to use as BASE before writing UdonSharp (prefer over inventing APIs) |
Changes observed during successful MCP inspections.
v2.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry the behavioral burden, and it does add useful context: examples returned should be adapted, not invented from, and validation should follow. However, it does not describe result format, pagination, or behavior when no matches are found, which are moderate gaps for a search tool.
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 compact and front-loaded with WHEN, HOW, and next-step directives. Every sentence contributes actionable workflow guidance without filler or repetition.
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 two-parameter search tool, the description covers when to call it, how to use the results, and what to do next. It is slightly thin on limit semantics and explicit sibling alternatives, but the essential invocation context is present.
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 query parameter is already described in the schema, and the description reinforces the intended usage without adding new parameter-level detail. The limit parameter has type, default, and range in the schema but no semantic explanation here, leaving a modest gap at 50% schema description coverage.
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 identifies the tool as a source of UdonSharp examples to use as a code base, which distinguishes it from documentation, template, and rule tools. It is clear about the resource and intended use, though it never literally states 'searches for examples' outside the schema.
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 explicitly says WHEN to use it ('before writing UdonSharp'), recommends pairing it with get_template, and specifies the follow-up action validate_code. It provides a clear workflow context, though it does not explicitly name sibling tools to avoid or describe when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.