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Glama

get_template

Fetch a base UdonSharp template before writing code. Adapt its fields and logic, avoid inventing networking, then call validate_code.

Instructions

WHEN: before writing UdonSharp. HOW: use the returned source as the BASE — adapt fields/logic; do NOT invent networking (ownership, Manual sync, RequestSerialization). After adapting, call validate_code.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
templateIdYesTemplate id/filename. Fetch BEFORE writing — adapt as base; do not invent networking.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.1.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral disclosure burden. It discloses that the returned value is a base template, not final code, and that networking must not be invented. This is meaningful context for an agent deciding how much to trust or extend the output. It does not discuss side effects, but the get_ prefix and template-retrieval nature make that low-risk.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely compact and front-loaded: WHEN, HOW, constraint, and next step in three short clauses. Every phrase earns its place, and the structure makes the intended workflow immediately clear.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter, no-output-schema tool, the description is nearly complete: it states when to use it, what to do with the result, a critical constraint, and the follow-up action. It does not mention where to find a valid templateId (e.g., list_templates), but the schema covers the parameter adequately.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents templateId. The parameter description adds 'filename' as a synonym and repeats the workflow guidance, but it does not meaningfully clarify what values are valid or how to obtain them. This is a baseline-3 case where the schema carries the load.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description conveys a specific purpose: fetch a template to use as the base for UdonSharp code before writing it. It clearly frames the tool as a source-template provider and even ties it to the next step, validate_code. It does not explicitly differentiate from list_templates or search_documentation, but the 'returned source as BASE' language gives enough specificity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit WHEN ('before writing UdonSharp') and HOW ('use the returned source as the BASE — adapt fields/logic') guidance. It also instructs what not to do (invent networking) and what to do next (call validate_code). It stops short of naming alternatives or stating when not to use this tool, so it is not a full 5.

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