Skip to main content
Glama

List Prompts

list_prompts

List all prompts exposed by a connected MCP server, including names, descriptions, and argument schemas, to enable accurate prompt invocation.

Instructions

List all prompts available on the connected MCP server.

Retrieves comprehensive information about all prompts exposed by the target server, including names, descriptions, and complete argument schemas to enable accurate prompt invocation.

Returns: Dictionary with prompt listing including: - success: True on successful retrieval - prompts: List of prompt objects with name, description, and arguments schema - metadata: Total count, server info, timing information

Raises: Returns error dict if not connected or retrieval fails

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.5.2
    • addedInput schema / additionalProperties
      Added value: +false
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral transparency burden. It discloses the return shape (success, prompts, metadata) and error behavior on connection failure or retrieval failure. It does not explicitly state that the operation is read-only, though the 'list' and 'retrieves' wording implies it.

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?

The description is well-structured with separate purpose, returns, and raises sections. It is slightly redundant, repeating 'all prompts' and 'server' across sentences, but overall it is focused and easy to scan.

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?

The description is complete for a simple, parameterless listing tool: it specifies what is returned, the included fields, and error behavior. Since an output schema exists, detailed return-value documentation is not required, and the prose sufficiently covers the tool's purpose and failure modes.

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?

The input schema has zero parameters, so the baseline is 4. The description adds no parameter-level detail because none is needed; it instead clarifies that the tool returns all prompts without filtering. This is appropriate for a parameterless tool.

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 the action ('List all prompts') and the target resource ('available on the connected MCP server'). It also distinguishes the tool from siblings by emphasizing enumeration of all prompts with names and argument schemas, rather than fetching a single prompt.

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 description makes it clear that this tool is for listing all prompts and gathering invocation metadata, which is useful context. However, it does not explicitly contrast with related sibling tools like get_prompt or list_tools, so guidance on when *not* to use it is left implicit.

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