neuron_recipe_update

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

With no annotations provided, the description carries full disclosure burden and does substantial work: it reveals the non-obvious side effect that bundled recipes are forked to ~/.neuron/recipes/ before modification, meaning the operation is not an in-place edit for bundled recipes. It also signals persistence/mutation intent. It doesn't cover error behavior or existence preconditions, but the forking disclosure is genuinely valuable beyond what annotations would typically say.

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?

Three sentences, each with a distinct job: what the tool does, the critical side effect, and when to use it. Purpose is front-loaded and there is zero filler or repetition of schema content.

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 mutation tool with 4 parameters, no annotations, and no output schema, the description covers the essentials: operation scope, a significant side effect, and lifecycle timing. Minor gaps remain — it doesn't state that slug must reference an existing recipe or describe the success/error return shape — but these are small against the strong forking disclosure.

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?

Schema description coverage is 100%, so the baseline is 3. The description adds only a light semantic gloss by mapping 'agent instructions,' 'learnings,' and 'configuration' to the agent_md, learnings, and recipe_yaml parameters. It does not clarify partial-update semantics, though the schema already implies it by requiring only slug.

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 and resource: 'Update a recipe's agent instructions, learnings, or configuration,' which maps cleanly to the three content parameters. The lifecycle phrase 'after the evolve phase to persist improved strategy' further separates it from recipe creation and execution siblings. However, it never explicitly names a sibling alternative (e.g., neuron_recipe_create), so it stops short of full differentiation.

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 final sentence gives explicit usage timing: 'Use this after the evolve phase to persist improved strategy,' which tells an agent precisely when this tool applies in the recipe lifecycle. The forking clause also implicitly scopes it to bundled-recipe updates. It does not state when not to use it or name alternative tools, so it misses the top score.

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