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misakanet_search

Search a public failure-lesson index by error text or keyword to discover relevant debugging lessons from real engineering sessions.

Instructions

Search MisakaNet's public failure-lesson index by error text, keyword, or topic. Use when you need to discover relevant lessons and do not already know a lesson ID. Input semantics: query is required; domain optionally filters by lesson domain; top limits ranked results and defaults to 5. Output schema: JSON with results[] and source; each result is a ranked lesson summary that may include path, title, domain/status, score/rank, and match details depending on the active index. Error cases: missing query, unavailable search index, or no matches (empty results). Side effects: none. Auth: none. Rate limits: local stdio process only; callers should keep result counts small. Do not use for private log collection; search only with redacted snippets. Use misakanet_get_lesson for full content.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topNoMaximum ranked results to return. Defaults to 5; keep small for MCP context and latency.
queryYesRequired redacted error message, keyword, or topic (for example: 'pip install timeout' or 'DCO sign-off failed').
domainNoOptional domain filter such as devops, python, network, feishu, rag, fanuc, or mcp.
Behavior5/5

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

With no annotations, the description fully discloses side effects (none), auth (none), rate limits (local stdio only), error cases (missing query, unavailable index, empty results), and output structure details.

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?

Well-structured with front-loaded purpose, followed by input semantics, output, errors, side effects, auth, and rate limits. Slightly verbose but every sentence adds value.

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 no output schema, the description explains the JSON structure and what each result may contain. Covers all aspects needed for correct invocation and understanding.

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%, with all parameters described. The description adds extra context like default top=5 and redacted query examples, surpassing the baseline of 3.

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 tool searches 'MisakaNet's public failure-lesson index' by error text, keyword, or topic, distinguishing it from siblings like misakanet_get_lesson for full content.

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?

Explicitly says when to use (when needing to discover lessons without a known lesson ID) and when not to use (not for private log collection, only with redacted snippets), and points to misakanet_get_lesson as an alternative.

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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