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langchain-ai

LangSmith MCP Server

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by langchain-ai

get_prompt_by_name

Retrieve a specific prompt template from LangSmith by providing its exact name, enabling prompt reuse and management in language model workflows.

Instructions

Get a specific prompt by its exact name.

Args: prompt_name (str): The exact name of the prompt to retrieve ctx: FastMCP context (automatically provided)

Returns: Dict[str, Any]: Dictionary containing the prompt details and template, or an error message if the prompt cannot be found

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
prompt_nameYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses that the tool retrieves prompt details and templates, and may return an error if the prompt is not found. However, it lacks details on authentication needs, rate limits, or whether the operation is idempotent.

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 front-loaded with the core purpose, followed by structured sections for Args and Returns. Every sentence adds value: the first states the action, the Args clarify the parameter, and the Returns explain the output and error handling.

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 simplicity (1 parameter, no annotations, but has an output schema), the description is mostly complete. It explains the purpose, parameter, and return behavior. However, it could improve by addressing authentication or error specifics, though the output schema may cover return values.

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 0% description coverage, but the description compensates by explaining that 'prompt_name' must be the exact name of the prompt to retrieve. It adds meaningful context beyond the bare schema, though it does not specify format constraints (e.g., case sensitivity).

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 specific action ('Get a specific prompt') and resource ('by its exact name'), distinguishing it from sibling tools like 'list_prompts' (which lists all prompts) and 'push_prompt' (which creates/updates prompts). The verb 'retrieve' reinforces the read-only nature.

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 (to retrieve a specific prompt by exact name) versus alternatives like 'list_prompts' (for browsing all prompts). However, it does not explicitly state when NOT to use it or mention other potential alternatives like 'push_prompt' for creating prompts.

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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