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misakanet_submit_usage

Submit feedback that a public lesson resolved an issue. Requires lesson ID and outcome (solved, partial, not-helpful).

Instructions

[Experimental] Record that a public lesson helped with a problem. Use only after the user or calling agent explicitly chooses to submit usage feedback for a specific lesson. Input semantics: lesson_id is required; tool names the calling client; outcome should be solved, partial, not-helpful, or another short status. Output schema: JSON with lesson_id, tool, outcome, and status. Error cases: missing lesson_id. Side effects: currently returns a local placeholder report only; it does not send data externally, open GitHub issues, or publish lessons. Auth: none. Rate limits: local stdio process only; submit at most once per resolved incident. Do not include raw logs, prompts, file contents, or secrets.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toolNoCalling tool or client name, for example claude-code, cursor, codex, or aider.
outcomeNoShort result label such as solved, partial, or not-helpful.
lesson_idYesRequired ID of the lesson that helped, for example auto-merge-ci-pipeline.
Behavior5/5

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

With no annotations provided, the description fully discloses behavioral traits: experimental status, side effects (local placeholder, no external sending), auth (none), rate limits (once per incident), and prohibits sensitive data inclusion.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph but well-structured with sections for purpose, usage, input semantics, output, and side effects. It is thorough but not overly verbose for its content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description describes the output format (JSON with lesson_id, tool, outcome, status). It covers error cases and side effects. It is complete for a simple feedback tool.

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%, but the description adds value by specifying that lesson_id is required, tool names the client, and outcome expects specific labels (solved, partial, not-helpful). This enriches the schema definitions.

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 action ('Record that a public lesson helped with a problem') and the resource ('usage feedback for a lesson'). It distinguishes from sibling tools (misakanet_usage_status, misakanet_search, misakanet_get_lesson) by specifying the submit usage action.

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

Usage Guidelines4/5

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

The description explicitly says to use only after explicit user/agent choice and mentions error cases (missing lesson_id). It provides clear context but lacks explicit 'when not to use' or alternative tool references.

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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