Retrieve available LP direction recipes the canvas can generate from without a model call, so clients can choose a starting template for offline Section JSON generation.
List LP direction recipes the canvas can generate from. No model call.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive and closed-world, so safety is covered. The description adds genuinely new context beyond them with 'No model call', telling the agent this is cheap and fast with no LLM cost or latency, which matters when choosing between this and generate_lp_sections.
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?
Two short sentences, no filler, and the value proposition ('No model call') is placed immediately after the purpose. Nothing is repeated from the name or title.
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?
For a zero-parameter lister with no output schema, the description should hint at what comes back – recipe names or IDs – since the agent needs those to call get_recipe next. That chaining detail is absent, though the read-only and no-model-call facts are complete.
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 tool takes zero parameters, so there is nothing for the description to disambiguate; baseline is 4. Schema coverage is 100% and additionalProperties is false, leaving no ambiguity about inputs.
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?
States a specific verb ('List') and resource ('LP direction recipes the canvas can generate from'), which distinguishes it from the generation sibling generate_lp_sections. It stops short of explicitly contrasting with get_recipe, and 'LP direction recipes' is domain jargon an agent may not resolve.
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 framing 'recipes the canvas can generate from' implies this is the discovery step preceding get_recipe or generate_lp_sections, but no alternative or trigger condition is named. Usage is inferable rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.