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MagneticDogSon

FixFlow

by MagneticDogSon
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

resolve_kb_id

Search the collective Knowledge Base for solutions to technical errors, bugs, or architectural patterns. Use as first step in any troubleshooting workflow.

Instructions

FIRST STEP in any troubleshooting workflow. Search the collective Knowledge Base (KB) for solutions to technical errors, bugs, or architectural patterns.

Uses full-text search across titles, content, tags, and categories. Results are ranked by relevance and success rate.

WHEN TO USE:

  • ALWAYS call this first when encountering any error message, bug, or exception.

  • Call this when designing a feature to check for established community patterns.

INPUT:

  • query: A specific error message, stack trace fragment, library name, or architectural concept.

  • category: (Optional) Filter by category (e.g., 'devops', 'terminal', 'supabase').

OUTPUT:

  • Returns a list of matching KB cards with their kb_id, titles, and success metrics.

  • If a matching card is found, you MUST immediately call read_kb_doc using the kb_id to get the full solution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo
categoryNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.8/5.0
Behavior4/5

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

No annotations provided, so description carries full burden. It describes search behavior, output structure, and that results are ranked, but does not mention any side effects or rate limits, which are expected for a search tool.

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?

Well-structured with clear sections (WHEN TO USE, INPUT, OUTPUT). Every sentence provides value; no superfluous text.

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 and lack of annotations, the description covers purpose, usage, parameters, output, and post-call actions. It also references sibling tools appropriately.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Adds significant meaning beyond the schema: explains that 'query' can be an error message or concept, and 'category' is an optional filter. With 0% schema coverage, the description fully compensates.

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?

Clearly states it is the first step in troubleshooting, performs full-text search across the Knowledge Base, and distinguishes itself from siblings like read_kb_doc and save_kb_card.

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 states 'ALWAYS call this first' for error messages, bugs, or design patterns, and instructs to follow up with read_kb_doc if a match is found.

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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