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Retrieve a compact index of available tools with descriptions and parameter schemas for LLM function-calling. Supports openai and anthropic formats. Use before agent planning to discover 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
formatNoOutput format for function calling.aicoreutils
rawNoWrite tools JSON directly without a JSON envelope.
Behavior4/5

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

Annotations already declare readOnlyHint=true, so the bar for behavioral transparency is lower. Description adds 'Read-only, no side effects' which aligns with annotations and provides additional safety context. No contradictions; the description reinforces and slightly extends the annotation.

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?

Four sentences with no filler. First sentence front-loads the purpose. Every sentence contributes essential information (read-only nature, default output, format options, usage context, sibling reference).

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 no output schema, description adequately explains the return value (JSON with tool names, descriptions, and parameter schemas). Format options are covered. All critical aspects for an agent to use the tool are present.

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 meaning beyond schema by explaining the format parameter options ('--format=openai for OpenAI-compatible function definitions, --format=anthropic for Anthropic tool format') and default behavior. The raw parameter is not addressed, but description still adds significant value.

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?

Description clearly states the verb ('Return'), resource ('compact tool index'), and purpose ('optimized for LLM function-calling context windows'). It distinguishes from sibling tools by explicitly naming 'catalog' and 'coreutils' as alternatives.

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?

Provides explicit when-to-use ('Use before agent planning to discover available capabilities') and when-not-to-use ('Not for human browsing — use 'catalog' for category-organized listing'), along with an alternative tool name.

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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