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MisakaNet

by Ikalus1988
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Python

misakanet_submit_intake

Submit a failure-case intake when no lesson matches or an existing lesson is incorrect, so the gap gets queued for review.

Instructions

Submit a failure-case intake when no matching lesson exists or a lesson was stale/incorrect. Use after misakanet_search fails to find a good match, or when the user resolved a problem not yet documented. Input semantics: problem is required (short description of the failure); kind defaults to missing_lesson; error, what_tried, fix, verification, and matched_lesson_id are optional. Output schema: JSON with submitted (boolean), intake_id, status (pending_review), redactions_applied, quality_score, and receipt. Error cases: missing problem, duplicate submission. Side effects: writes to data/contribution_queue.jsonl. Auth: none. Rate limits: local stdio process only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fixNoOptional: how the problem was resolved, if known.
kindNoType of intake. missing_lesson = no match found; stale_lesson = matched but wrong; new_lesson_candidate = user resolved a new problem.
errorNoOptional short error message.
sourceNoCalling client: codex, claude-code, cursor, dsh, curl, or other.
problemYesRequired short description of the failure or gap (max 2000 chars).
what_triedNoOptional: what was attempted before or during the failure.
verificationNoOptional: how to confirm the fix works.
matched_lesson_idNoOptional: lesson ID that was checked but did not help (for stale_lesson).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv2.21.0
    • changedInput schema / properties / error / description
      Previous value: -"Optional short error message (auto-redacted)."New value: +"Optional short error message."
  2. Addedv2.17.1

TDQS

A4.7/5.0
Behavior5/5

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

No annotations are present, so the description must carry the burden, and it does: it declares the side effect (writes to data/contribution_queue.jsonl), error cases (missing problem, duplicate submission), output schema fields, auth none, and rate limits. This goes well beyond minimal disclosure.

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?

Every sentence adds distinct information, and the internal labels (Input semantics, Output schema, Error cases, Side effects, Auth, Rate limits) make scanning easy. The most important purpose and usage 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?

For a tool with no annotations and no output schema, the description covers all invocation-critical aspects: inputs, output shape, errors, side effects, auth, and rate limits. Nothing an agent needs to call it safely and correctly is missing.

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 the baseline is 3; the description adds the default for kind (missing_lesson) and the required/optional split. It omits 'source' from its summary, but the schema already documents it, so this is a minor gap.

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 opens with a specific verb ('Submit') and resource ('a failure-case intake') and immediately states the triggering conditions ('no matching lesson exists or a lesson was stale/incorrect'). This clearly separates it from sibling tools like misakanet_search and misakanet_write_lesson.

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?

It gives explicit 'Use after misakanet_search fails...' and 'when the user resolved a problem not yet documented' triggers. It does not name exclusions or contrast with other submission tools like submit_usage/write_lesson, so it stops short of a full 5.

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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