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Ikalus1988

MisakaNet

by Ikalus1988
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Python

misakanet_search

Search a public failure-lesson index by error text, keyword, or topic to find relevant debugging lessons without knowing a lesson ID.

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. kind filters by result type: 'lessons' (lesson files only), 'evidence' (results with evidence_refs or verification), 'related' (cross-referenced/tag-overlap), 'all' (default). kind is auto-detected from query intent when omitted (e.g. 'lesson about X' → lessons, 'evidence for X' → evidence). Set explain=true to return matched terms, TF-IDF, entity matches, vector similarity, and hybrid score components. detail controls progressive disclosure: compact (default, ~80 tok/lesson) for broad scans, summary (~200 tok) with domain/tags/fix, full for complete lesson markdown. Output schema: JSON with results[] and source; each result is a ranked lesson summary. 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.
kindNoFilter results by kind: 'lessons' returns only lesson files, 'evidence' returns results with evidence_refs or high evidence_level, 'related' returns cross-referenced/tag-overlap results. Default 'all' returns everything. Auto-detected from query intent when omitted (e.g. 'lesson about X' → lessons, 'evidence for X' → evidence).
queryYesRequired redacted error message, keyword, or topic (for example: 'pip install timeout' or 'DCO sign-off failed').
detailNoProgressive disclosure: compact (default, ~80 tok/lesson) shows id/title/problem/freshness; summary (~200 tok) adds domain/tags/fix; full returns complete lesson markdown. Use compact for broad scans, full only after narrowing results.
domainNoOptional domain filter such as devops, python, network, feishu, rag, fanuc, or mcp.
explainNoInclude score evidence for each result; vector similarity is null when the optional backend is unavailable.
bm25_weightNoOverride BM25 keyword weight (0-1). Higher values favor exact keyword matches. Default: 0.65. All weights must sum to 1.0.
include_staleNoInclude stale and superseded lessons in results. Default false — these are filtered out to avoid误导 agents with outdated information.
baseline_weightNoOverride baseline score weight (0-1). Higher values favor proven/popular lessons. Default: 0.15.
metadata_weightNoOverride metadata bonus weight (0-1). Higher values favor lessons with matching domain/tags. Default: 0.20.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv2.28.0
    • addedInput schema / properties / include_stale
      Added value: +{
+  "description": "Include stale and superseded lessons in results. Default false — these are filtered out to avoid误导 agents with outdated information.",
+  "type": "boolean"
+}
    • addedInput schema / properties / kind
      Added value: +{
+  "description": "Filter results by kind: 'lessons' returns only lesson files, 'evidence' returns results with evidence_refs or high evidence_level, 'related' returns cross-referenced/tag-overlap results. Default 'all' returns everything. Auto-detected from query intent when omitted (e.g. 'lesson about X' → lessons, 'evidence for X' → evidence).",
+  "enum": [
+    "all",
+    "lessons",
+    "evidence",
+    "related"
+  ],
+  "type": "string"
+}
  2. Changed3 schema fields changedv2.23.0
    • addedInput schema / properties / baseline_weight
      Added value: +{
+  "description": "Override baseline score weight (0-1). Higher values favor proven/popular lessons. Default: 0.15.",
+  "type": "number"
+}
    • addedInput schema / properties / bm25_weight
      Added value: +{
+  "description": "Override BM25 keyword weight (0-1). Higher values favor exact keyword matches. Default: 0.65. All weights must sum to 1.0.",
+  "type": "number"
+}
    • addedInput schema / properties / metadata_weight
      Added value: +{
+  "description": "Override metadata bonus weight (0-1). Higher values favor lessons with matching domain/tags. Default: 0.20.",
+  "type": "number"
+}
  3. Changed1 schema field changedv2.21.0
    • addedInput schema / properties / detail
      Added value: +{
+  "description": "Progressive disclosure: compact (default, ~80 tok/lesson) shows id/title/problem/freshness; summary (~200 tok) adds domain/tags/fix; full returns complete lesson markdown. Use compact for broad scans, full only after narrowing results.",
+  "enum": [
+    "compact",
+    "summary",
+    "full"
+  ],
+  "type": "string"
+}
  4. Changed1 schema field changedv2.17.1
    • addedInput schema / properties / explain
      Added value: +{
+  "description": "Include score evidence for each result; vector similarity is null when the optional backend is unavailable.",
+  "type": "boolean"
+}
  5. Changed3 schema fields changedv2.14.0
    • changedInput schema / properties / domain / description
      Previous value: -"Optional domain filter (devops, python, network, feishu, rag, fanuc, etc.)"New value: +"Optional domain filter such as devops, python, network, feishu, rag, fanuc, or mcp."
    • changedInput schema / properties / query / description
      Previous value: -"Search query — error message, keyword, or topic (e.g. 'pip install timeout', 'DCO sign-off failed')"New value: +"Required redacted error message, keyword, or topic (for example: 'pip install timeout' or 'DCO sign-off failed')."
    • changedInput schema / properties / top / description
      Previous value: -"Max results to return (default 5)"New value: +"Maximum ranked results to return. Defaults to 5; keep small for MCP context and latency."
  6. Addedv2.12.4
  7. Removedv2.12.2
  8. First observedv2.12.3

TDQS

A4.8/5.0
Behavior5/5

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

With no annotations, the description carries full behavioral disclosure. It explicitly states 'Side effects: none. Auth: none. Rate limits: local stdio process only; callers should keep result counts small.' It also covers error cases, output format, and the vector similarity null when the optional backend is unavailable. This is thorough and transparent.

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 long but well-structured and front-loaded with the core purpose and usage context. It then systematically explains parameters, output, errors, and side effects. While it could be tightened, every section adds necessary information, and the organization makes it scannable.

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?

Given the tool's complexity (10 parameters, no output schema), the description covers all essential aspects: query requirements, filtering options, progressive disclosure levels, error cases, output format, side effects, auth, rate limits, and privacy constraints. It even explains when to use the sibling for full content. Nothing critical 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 value beyond the schema by explaining the auto-detection of 'kind' from query intent, the progressive disclosure trade-offs of 'detail', and the interaction of weight parameters (though the schema already mentions the sum constraint). It enriches understanding of when and how to set parameters.

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 and resource: 'Search MisakaNet's public failure-lesson index by error text, keyword, or topic.' It clearly states the scope (discovery, not retrieval by ID) and explicitly contrasts with the sibling misakanet_get_lesson, which is for full content when an ID is known. This makes the tool's role unambiguous.

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?

It provides explicit when-to-use guidance: 'Use when you need to discover relevant lessons and do not already know a lesson ID.' It also tells when not to use it: 'Do not use for private log collection; search only with redacted snippets.' It names the alternative for full content: 'Use misakanet_get_lesson for full content.' No inference is required.

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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