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small-mcp

small-mcp

by Nishant-IIT
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_prompt

Retrieve a named prompt with optional arguments and receive the rendered output as JSON containing a messages array.

Instructions

Get a prompt by name with optional arguments.

Returns the rendered prompt as JSON with a messages array. Arguments should be provided as a dict mapping argument names to values.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

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

Input Schema

NameRequiredDescriptionDefault
nameYesThe name of the prompt to get
argumentsNoOptional arguments for the prompt

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral transparency burden. It explains the return format (JSON with a messages array) and clarifies how arguments should be structured (dict mapping names to values). It does not cover error cases or side effects, but for a simple get operation this is adequate.

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 two sentences, front-loaded with the primary action ('Get a prompt by name'), followed by concise return-format and argument-format details. Every sentence adds value with no redundancy or filler.

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 has only two parameters, an output schema, and no annotations, the description covers the essential behavioral aspects: what it retrieves, how to pass arguments, and what the return shape looks like. It is sufficiently complete for an agent to invoke the tool correctly without further context.

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 already provides descriptions for both parameters (name and arguments), giving 100% coverage. The description adds extra meaning by specifying that arguments must be a dict mapping argument names to values, which is not fully detailed in the schema. This elevates the score above the baseline.

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 tool fetches a prompt by name, with optional arguments. This distinguishes it from sibling tools like list_prompts (which would enumerate prompts) and call_tool (which invokes tools). The verb-resource pair is specific and unambiguous.

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 implies when to use this tool: when you know the prompt's name and want its rendered output. It does not explicitly mention alternatives or provide 'when not to use' guidance, but the context is clear enough for an agent to decide appropriately.

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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