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get_skill_catalog

Read-onlyIdempotent

Retrieve the complete catalog of skills and tools for designing workflows, and use the returned names to create or update workflow drafts.

Instructions

[READ] Get the complete catalog of available skills and tools for workflow design.

Use this to understand what building blocks are available when designing a custom workflow, then feed the skill and tool names into create_workflow or update_draft steps. Note this is a static curated catalog, not a live query of each skill's registry, so it may lag a skill's actual tool list; pilot cannot call these tools itself — the calling agent does.

Returns: dict mapping skill name → {description, tools: {tool_name: {risk, desc}}}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

Even though annotations already provide readOnlyHint and idempotentHint, the description adds unique behavioral context: the catalog is static and may lag the actual registry, and that the calling agent (not the pilot) must invoke the listed tools. It also discloses the return structure, giving the agent a clear expectation of the output.

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 a bold READ tag, early statement of purpose, then usage guidance and a note on limitations, followed by a compact return format. Every sentence adds necessary value, and the most critical information is front-loaded.

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 no output schema, the description fully documents the return type ('dict mapping skill name → {description, tools: {tool_name: {risk, desc}}}'). It also explains the tool's role in the workflow design process, making it complete for an agent to select and use the tool correctly.

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?

The tool has zero parameters, so the schema trivially covers 100%. The description does not need to explain parameters. Baseline for zero params is 4, and no additional parameter semantics are required.

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 begins with '[READ] Get the complete catalog of available skills and tools for workflow design,' which names a specific verb, resource, and context. This clearly distinguishes it from sibling tools like create_workflow and run_workflow that perform actions rather than provide an inventory.

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?

It explicitly states when to use the tool ('when designing a custom workflow') and how to use the result ('feed the skill and tool names into create_workflow or update_draft steps'). It also clarifies limitations (static catalog may lag, pilot cannot call tools) which prevents misuse, going beyond generic guidance.

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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