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Glama

fixflow

by io.github.MagneticDogSon

resolve_kb_id

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

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.8/5.0
Behavior4/5

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

With no annotations provided, the description carries the burden and discloses key behaviors: full-text search across specific fields, ranking by relevance/success rate, and output format. It stops short of detailing edge cases like empty results or rate limits, so a perfect score is not given.

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?

The description is well-structured with clear sections (FIRST STEP, WHEN TO USE, INPUT, OUTPUT). Every sentence provides useful information, and it is front-loaded with the tool's primary purpose. No wasted words.

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, it fully covers purpose, usage, inputs, outputs, and the required next step (calling read_kb_doc). The presence of an output schema is acknowledged but the description still explains the return value meaning, making it self-contained.

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?

Schema coverage is 0%, but the description fully compensates by explaining the query parameter as a specific error message/stack trace/etc., and category as an optional filter with examples. This adds significant meaning beyond the bare schema.

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 identifies the tool as a KB search function using the verb 'search' and specifies the resource ('collective Knowledge Base'). It is explicitly marked as the 'FIRST STEP' and distinct 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?

A dedicated 'WHEN TO USE' section provides explicit guidance: always call first for errors/bugs, and also when designing features. It also instructs to follow up with read_kb_doc after getting a kb_id, effectively distinguishing it from sibling tools.

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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TDQS

A4.8/5.0
Disambiguation5/5

Each tool has a distinct role in the troubleshooting workflow: resolve_kb_id searches, read_kb_doc retrieves a specific card, and save_kb_card writes or reports outcomes. There is no overlap in their purposes.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern: resolve_kb_id, read_kb_doc, save_kb_card. The verbs clearly indicate the action taken on the knowledge base.

Tool Count5/5

Three tools perfectly cover the search-read-write lifecycle of a knowledge base. The count is well-scoped for the server's specific purpose, avoiding unnecessary bloat.

Completeness5/5

The toolset provides complete coverage for the stated workflow: search, read full content, save new cards, and report outcomes. The overwrite option in save_kb_card also supports updates, closing the loop.

Resources