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GroundTruth Dispatch

gt_dispatch
Read-onlyIdempotent

Routes ambiguous user queries to the appropriate tool by analyzing intent and selecting the correct tool and arguments.

Instructions

Routes a plain-text user query to the correct gt_* tool with the right arguments. Examples: "use gt", "use gt for react", "find issues in this codebase", "migrate next from 14 to 15".

WHEN TO USE: the user's intent is ambiguous, they invoked gt without specifying a tool ("use gt mcp"), or you want a single entry point that always returns something actionable.

WHEN NOT TO USE: you already know which gt_* tool fits. Call it directly to save one round-trip.

OUTPUT: a routing decision with tool name, args, reason, and a 0-to-1 confidence score. The response text also embeds the routing table and a recommended JSON call so you can make the next tool call without another lookup.

Use it for "use gt mcp" in any phrasing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesPlain-text user intent. Examples: 'use gt for react', 'find issues', 'migrate next from 14 to 15', 'best practices for fastapi'.
projectPathNoOptional project directory for project-level intents (auto-scan, audit). Defaults to current working directory.
Behavior5/5

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

Annotations declare readOnly, non-destructive, idempotent, and closed-world hints. The description adds behavioral context: output includes routing decision with confidence, tool name, args, and an embedded routing table for next-step calls.

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, examples, and no superfluous text. It is front-loaded with the core purpose.

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 role as a dispatcher, the description, annotations, and schema together provide complete context: purpose, usage boundaries, parameter behavior, and output expectations (no output schema needed due to textual description).

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 coverage is 100%, so baseline is 3. The description adds query examples and optional nature of projectPath, but the schema already describes parameters adequately.

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 it routes plain-text queries to the correct gt_* tool, with specific examples. It distinguishes from sibling tools by positioning itself as a single entry point when the target is unknown.

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?

Explicit 'WHEN TO USE' and 'WHEN NOT TO USE' sections clearly state when to use this dispatcher vs. calling a specific sibling directly, including example phrasings.

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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