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get_prompt

Retrieve rendered prompt messages from the MCP Test MCP server by specifying a prompt name and required arguments.

Instructions

Get a rendered prompt from the connected MCP server.

Retrieves a prompt by name with the provided arguments and returns the rendered prompt messages.

Returns: Dictionary with rendered prompt including: - success: True if prompt was retrieved successfully - prompt: Object with name, description, and rendered messages - metadata: Request timing and server information

Raises: Returns error dict for various failure scenarios: - not_connected: No active connection - prompt_not_found: Prompt doesn't exist on server - invalid_arguments: Arguments don't match prompt schema - execution_error: Prompt retrieval failed

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesName of the prompt to retrieve
argumentsYesDictionary of arguments to pass to the prompt

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: it's a retrieval operation (implied by 'Get'), requires a connection (via 'not_connected' error), and handles various failure scenarios. However, it doesn't mention rate limits, caching behavior, or whether it's idempotent, which are gaps for a tool with no annotations.

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 appropriately sized and front-loaded with the core purpose in the first sentence. The 'Returns' and 'Raises' sections are structured but slightly verbose; the error cases could be more concise. Overall, most sentences earn their place by clarifying behavior and outputs.

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?

Given the tool's moderate complexity (2 parameters, no annotations, but with an output schema), the description is fairly complete. It explains the purpose, return structure, and error cases. Since an output schema exists, it doesn't need to detail return values extensively. However, it lacks usage context and some behavioral details, preventing a perfect score.

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 100% description coverage, so the baseline is 3. The description adds no additional meaning beyond what the schema provides for parameters 'name' and 'arguments'. It mentions 'arguments' in the context of matching a prompt schema, but this is covered by the 'invalid_arguments' error case rather than parameter semantics.

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 tool's purpose: 'Get a rendered prompt from the connected MCP server' with specific verb ('Get') and resource ('rendered prompt'). It distinguishes from siblings like 'list_prompts' (which lists rather than retrieves) and 'execute_prompt_with_llm' (which executes rather than just retrieves). However, it doesn't explicitly contrast with all siblings, so it's not a perfect 5.

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. It doesn't mention when to use 'get_prompt' versus 'list_prompts' (for listing available prompts) or 'execute_prompt_with_llm' (for executing a prompt with an LLM). There's no context about prerequisites like needing a connection first, though this is implied by the error cases.

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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