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List prompts

list_prompts
Read-only

List the caller's saved AI prompts (their prompt library) from their active workspace, newest first. Read-only. Optionally filter by 'category' or a text 'query' over title/body. Reusable lead reply templates live here by convention under category 'lead-reply' — pass category='lead-reply' to fetch just those.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_prompts"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
queryNo
categoryNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and destructiveHint=false, so safety is covered; the description still adds real context beyond them: workspace scoping, newest-first ordering, and the 'lead-reply' categorization convention. It does not mention pagination or result shape, which is the remaining gap.

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?

Three tight sentences, front-loaded with the core action before the filter and convention details. 'Read-only' mildly duplicates the annotations, but the rest of the text earns its space.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple, non-destructive list tool with no output schema and no required parameters, the description covers scope, ordering, and both meaningful filters adequately. Pagination behavior and return shape are the only unaddressed items, and both are low-risk here.

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

Parameters4/5

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

With 0% schema description coverage the description must carry the load, and it defines 'query' as a text search over title and body and 'category' as a categorical filter with a concrete convention value. Only 'limit' is left unexplained, though its 1–100 bounds in the schema make it largely self-evident.

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 and resource ('List the caller's saved AI prompts / prompt library') plus scope ('from their active workspace, newest first'), so it is immediately distinguishable from the many sibling list tools that target other resources.

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?

Clearly frames this as the library-listing read and gives an explicit conditional path ('pass category=lead-reply to fetch just those'), which tells the agent how to narrow to reusable templates. It doesn't name an alternative tool or state when not to use this one, which keeps it short of a 5.

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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