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list_prompts

Browse available prompt templates by category to find reusable workflows for tasks like daily planning, code review, and document summarization.

Instructions

List all available prompt templates, optionally filtered by category

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryNoFilter by category

Implementation Reference

  • Handler function for the 'list_prompts' tool. Retrieves prompts from the registry (filtered by optional category), formats them into a JSON structure with count and details (id, name, description, category, param count, tags), and returns as text content.
    async (args) => {
      const prompts = registry.prompts.list(args.category as PromptCategory);
    
      return {
        content: [
          {
            type: "text",
            text: JSON.stringify(
              {
                count: prompts.length,
                prompts: prompts.map((p) => ({
                  id: p.id,
                  name: p.name,
                  description: p.description,
                  category: p.category,
                  parameters: p.parameters.length,
                  tags: p.tags,
                })),
              },
              null,
              2
            ),
          },
        ],
      };
    }
  • Input schema for 'list_prompts' tool using Zod. Defines an optional 'category' parameter enum with predefined prompt categories.
    {
      category: z
        .enum(["productivity", "code", "research", "communication", "general"])
        .optional()
        .describe("Filter by category"),
    },
  • Registration of the 'list_prompts' MCP tool on the McpServer instance within registerPromptTools function.
    server.tool(
      "list_prompts",
      "List all available prompt templates, optionally filtered by category",
      {
        category: z
          .enum(["productivity", "code", "research", "communication", "general"])
          .optional()
          .describe("Filter by category"),
      },
      async (args) => {
        const prompts = registry.prompts.list(args.category as PromptCategory);
    
        return {
          content: [
            {
              type: "text",
              text: JSON.stringify(
                {
                  count: prompts.length,
                  prompts: prompts.map((p) => ({
                    id: p.id,
                    name: p.name,
                    description: p.description,
                    category: p.category,
                    parameters: p.parameters.length,
                    tags: p.tags,
                  })),
                },
                null,
                2
              ),
            },
          ],
        };
      }
    );
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool lists prompts with optional filtering, but doesn't describe key behaviors such as whether it returns all prompts at once or uses pagination, what the output format looks like (e.g., list of objects with fields), or any rate limits or permissions required. For a list operation with zero annotation coverage, this is a significant gap.

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 a single, efficient sentence that front-loads the core action ('List all available prompt templates') and adds the optional filtering detail. There is no wasted text, and it's appropriately sized for the tool's complexity.

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

Completeness2/5

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

Given the tool's moderate complexity (listing with filtering), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the return values, behavioral traits like pagination or permissions, or how it differs from sibling tools. This leaves gaps for an AI agent to understand the tool fully in context.

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?

The input schema has 1 parameter with 100% description coverage, providing an enum for the 'category' filter. The description adds minimal value beyond the schema by mentioning 'optionally filtered by category', but doesn't elaborate on semantics like what categories mean or how filtering works. With high schema coverage, the baseline is 3, and the description doesn't significantly enhance parameter understanding.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('List') and resource ('all available prompt templates'), and mentions optional filtering by category. However, it doesn't explicitly distinguish this tool from sibling tools like 'get_prompt_categories' or 'search_prompts', which appear to be related to prompts. The purpose is clear but lacks sibling differentiation.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like 'get_prompt_categories' (which might list categories) or 'search_prompts' (which might search prompts). It mentions optional filtering but doesn't specify contexts or exclusions for usage, leaving the agent with no explicit when/when-not instructions.

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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