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gateonai-mcp-server

Match a Prompt to My Task

match_prompt_to_task
Read-only

Match a free-text task to verified prompts from GateOnAI’s library using BM25 full-text search; returns prompt text, intended tool, and profession.

Instructions

Given a free-text description of a task (e.g. 'write a cold email to a client'), finds the best-matching existing prompt(s) from GateOnAI's verified prompt library using real BM25 full-text search - not a semantic guess or an invented relevance score. Each result includes the actual prompt text, which tool it's designed for, and the profession it comes from.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
taskYesFree-text description of the task, e.g. 'write a cold email to a client' or 'summarize a legal contract'
limitNoNumber of matching prompts to return (1-10, default 5)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
toolYesName of the tool that produced this result
linksYesgateonai.com URLs referenced in the result, in order of appearance
is_errorYesTrue if the tool could not complete the request
markdownYesThe full result as Markdown (same as the text content), including GateOnAI's disclaimer

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.2.0

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=true, so the safety profile is covered. The description adds real value beyond that: it discloses the retrieval mechanism (BM25 full-text, explicitly not semantic scoring) and what each result contains (prompt text, target tool, profession), which shapes trust in the output.

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?

Front-loaded with the core action and an illustrative example, then a second sentence covering mechanism and return contents. Mostly tight, though the 'verified prompt library' and 'not a semantic guess' phrasing leans promotional rather than purely functional.

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?

With an output schema present, the description need not enumerate return fields in depth, yet it still summarizes what results contain. Combined with 100% parameter coverage and rich annotations, an agent has everything needed to call this 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 coverage is 100%, so both the task string and the limit range (1-10, default 5) are already documented in the schema. The description adds no syntax or format detail beyond the schema, which is the expected baseline when the schema carries the load.

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?

States a specific verb (finds/matches) and resource (existing prompts from a prompt library) with the input modality (free-text task description) and example. It also distinguishes itself from sibling get_prompts_for_profession by being task-driven rather than profession-driven.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The trigger condition is clear: supply a free-text task description when you want matching prompts. However, it never names an alternative tool (e.g. get_prompts_for_profession) or states when NOT to use it, so guidance stops short of explicit routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.