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Return a compact JSON index of tool names, descriptions, and parameter schemas, optimized for LLM function-calling context windows. Use before agent planning to discover available capabilities.

Instructions

Return a compact tool index optimized for LLM function-calling context windows. Read-only, no side effects. Returns JSON with tool names, descriptions, and parameter schemas by default. Use --format=openai for OpenAI-compatible function definitions, --format=anthropic for Anthropic tool format. Use before agent planning to discover available capabilities. Not for human browsing — use 'catalog' for category-organized listing. See also 'catalog', 'coreutils'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rawNoWrite tools JSON directly without a JSON envelope.
formatNoOutput format for function calling.aicoreutils
include_riskNoInclude aicoreutils risk metadata in tool-list output.
Behavior5/5

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

Annotations already declare readOnlyHint=true; description adds 'Read-only, no side effects' and explains output formats and risk metadata, providing comprehensive behavioral context without contradiction.

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?

Description is compact (4 sentences), front-loaded with purpose, and every sentence provides essential information without redundancy.

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?

Given 3 optional parameters, no output schema, and clear annotations, the description fully covers purpose, usage, format options, and alternatives, leaving no gaps for an AI agent.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. Description adds value by explaining usage of format options (e.g., --format=openai) and the include_risk parameter, which goes beyond the schema's basic descriptions.

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 the tool returns a compact tool index for LLM function-calling contexts, specifies verb and resource, and distinguishes from siblings like 'catalog' and 'coreutils'.

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

Usage Guidelines5/5

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

Explicitly says when to use ('before agent planning') and when not ('Not for human browsing — use catalog'), and suggests alternatives ('See also catalog, coreutils').

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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