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get_license_details

Retrieve comprehensive details for any SPDX license, including full text, permissions, and obligations to ensure compliance.

Instructions

Get comprehensive details about a specific license.

This tool provides complete license information including the full license text for generating NOTICE files and understanding license requirements.

Args: license_id: SPDX license ID (e.g., "Apache-2.0", "MIT", "GPL-3.0") include_full_text: Include full license text (can be long, ~5-20KB)

Returns: License information including: - name: Full license name - type: License category (permissive, copyleft_weak, copyleft_strong, etc.) - properties: Characteristics (OSI approved, FSF free, etc.) - permissions: What you CAN do (commercial use, modify, distribute, etc.) - requirements: What you MUST do (include license, preserve copyright, etc.) - limitations: What is NOT provided (liability, warranty, etc.) - obligations: Specific compliance requirements - full_text: Complete license text (if include_full_text=True, fetched from SPDX API)

Example: For Apache-2.0, returns complete license data including: - Full license text for NOTICE files - Patent grant information - Attribution requirements

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
license_idYes
include_full_textNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description covers key behavior: it retrieves data from an external API (SPDX), warns about large text size (~5-20KB), and indicates optional full-text inclusion. It doesn't mention auth or rate limits, but the disclosure is strong.

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 well-structured with sections for Args, Returns, and Example, using clear language. Every sentence adds value without redundancy, and the size is appropriate for the tool's detail level.

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

Completeness5/5

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

Despite having an output schema, the description provides rich context about return fields and usage. It covers all necessary aspects for an agent to use this tool correctly, especially given the sibling tools focusing on analysis rather than raw data.

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

Parameters5/5

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

Schema coverage is 0%, but the description provides complete parameter details: license_id with examples (Apache-2.0, MIT, GPL-3.0) and include_full_text with size warning and default. It also exhaustively lists return fields, adding substantial value beyond the bare schema.

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

Purpose5/5

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

The description clearly states it gets comprehensive details about a specific license, including full license text. It distinguishes from sibling tools like check_license_compatibility or get_license_obligations by focusing on raw data retrieval.

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 specifies it's for generating NOTICE files and understanding license requirements, providing clear usage context. It lacks explicit when-not-to-use or alternatives, but the purpose is well-defined.

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

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