Get Fabric Detail

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

Annotations cover read-only, non-destructive, idempotent, and closed-world hints, but the description adds valuable behavioral context beyond this. It details the return structure (30+ fields with specific examples like 'lab-tested weight'), error conditions ('Returns error if fabric_id not found'), constraints ('Do not call in a loop'), and data availability ('Unverified fabrics return "not available"'). This enriches the agent's understanding without contradicting 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 well-structured with clear sections (PREREQUISITE, USE WHEN, RETURNS, etc.) and front-loaded key information. However, it includes some redundancy (e.g., repeating 'lab-tested' and listing many field examples) and a duplicate Chinese translation at the end, slightly reducing efficiency. Most sentences earn their place by adding context or constraints.

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 (retrieving detailed fabric records) and lack of output schema, the description provides comprehensive context. It outlines the return data structure, error handling, usage constraints, and integration with sibling tools. This compensates for the missing output schema and aligns well with the rich annotations, making it complete enough for an agent to use effectively.

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 input schema has 100% description coverage, clearly documenting the single required parameter 'fabric_id' as a string from search_fabrics. The description reinforces this by stating 'Fabric ID from search_fabrics results' and providing an example ('e.g. FAB-W007'), but adds no significant semantic details beyond what the schema already provides. This meets the baseline for high schema 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 explicitly states the tool retrieves 'the complete lab-tested record of a single fabric by ID,' specifying the verb ('get'), resource ('fabric'), and scope ('complete lab-tested record'). It distinguishes from sibling tools like search_fabrics (which lists fabrics) and get_fabric_suppliers (which focuses on suppliers), making the purpose highly specific and clear.

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 provides explicit guidance on when to use this tool: 'USE WHEN user wants full specs on a specific fabric after search_fabrics identified it.' It also includes prerequisites ('MUST first call search_fabrics'), alternatives ('present search_fabrics summary results instead' for multiple fabrics), and workflow integration ('search_fabrics → pick fabric_id → get_fabric_detail'), leaving no ambiguity about proper usage.

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