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bshea-1

Routed

by bshea-1
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TypeScript
Local

route_skill

Route natural language prompts to matching agent skills using hybrid BM25 and dense embeddings, with local CPU execution and zero LLM context usage.

Instructions

Route natural language prompts to matching agent skills using hybrid BM25 and dense embeddings. Behavior: Read-only local CPU execution in sub-20ms with zero LLM context tokens. Usage Guidelines: Primary entry point. Use route_skill to match task prompts against skills. Use list_skills to browse skills without a prompt, get_skill for known IDs, or scan_skills to refresh index. Parameters: prompt is the required query; topK (1-10, default 3) sets result limit; host filters environment; explain enables scoring breakdown signals.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hostNoOptional host environment filter to restrict matches to a specific AI tool (e.g. cursor, antigravity, claude-code, gemini-cli, hermes, codegate, openclaw, openmanus, lmstudio, ollama).
topKNoMaximum number of top-matching skills to return. Valid integer range: 1 to 10 (default: 3).
promptYesThe natural language user prompt, coding task, or question to route to relevant skills (required, non-empty string).
explainNoWhen true, includes scoring breakdown signals (exact match score, BM25 lexical score, vector semantic similarity). Default: false.

Schema Changelog

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

  1. Changed1 schema field changedv1.0.2
    • changedInput schema / properties / topK / description
      Previous value: -"Maximum number of top-matching skills to return. Valid range: 1 to 10 (default: 3)."New value: +"Maximum number of top-matching skills to return. Valid integer range: 1 to 10 (default: 3)."
  2. Changed4 schema fields changedv1.0.1
    • changedInput schema / properties / explain / description
      Previous value: -"Include scoring breakdown and matched signals."New value: +"When true, includes scoring breakdown signals (exact match score, BM25 lexical score, vector semantic similarity). Default: false."
    • changedInput schema / properties / host / description
      Previous value: -"Optional host environment filter (e.g. antigravity, cursor, claude-code, lmstudio, ollama)."New value: +"Optional host environment filter to restrict matches to a specific AI tool (e.g. cursor, antigravity, claude-code, gemini-cli, hermes, codegate, openclaw, openmanus, lmstudio, ollama)."
    • changedInput schema / properties / prompt / description
      Previous value: -"The user prompt or task description to route."New value: +"The natural language user prompt, coding task, or question to route to relevant skills (required, non-empty string)."
    • changedInput schema / properties / topK / description
      Previous value: -"Maximum number of skills to return (default: 3)."New value: +"Maximum number of top-matching skills to return. Valid range: 1 to 10 (default: 3)."
  3. First observedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

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

Since no annotations are provided, the description carries the full burden of behavioral disclosure, and it delivers: read-only execution, local CPU operation, sub-20ms latency, and zero LLM context tokens. These are concrete, useful behavioral traits that an agent needs to decide whether to call this 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?

The description is compact, uses labeled sections for behavior, usage guidelines, and parameters, and contains no filler. Every sentence contributes to either tool selection, invocation, or safety/performance understanding.

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 routing tool with no output schema, the description covers the core purpose, behavior, alternatives, and parameter semantics. An agent has enough context to decide when to use it and how to call it correctly.

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

Parameters3/5

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

Schema description coverage is 100%, so the baseline is 3. The description summarizes each parameter accurately, but adds little beyond what the schema already states about prompt, topK, host, and explain.

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 states a clear verb and resource: route natural language prompts to matching agent skills, with the matching mechanism specified as hybrid BM25 and dense embeddings. It also distinguishes itself from siblings by explicitly naming list_skills, get_skill, and scan_skills as alternatives with different purposes.

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?

The description explicitly labels route_skill as the primary entry point and gives concrete conditions for using each sibling tool: list_skills for browsing without a prompt, get_skill for known IDs, and scan_skills for refreshing the index. This gives an agent clear decision criteria for tool selection.

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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