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avivsinai

langfuse-mcp

get_prompt_unresolved

Fetch a prompt's raw content with dependency tags intact for analyzing composition and debugging dependency chains.

Instructions

Fetch a specific prompt by name WITHOUT resolving dependencies.

Returns raw prompt content with dependency tags intact (e.g., @@@langfusePrompt:name=xxx@@@) when
the SDK supports resolve=false. Otherwise returns the resolved prompt and marks metadata.resolved=True.
Useful for analyzing prompt composition and debugging dependency chains.

Args:
    ctx: Context object containing lifespan context with Langfuse client
    name: The name of the prompt to fetch
    label: Optional label to fetch. Cannot be used with version.
    version: Optional specific version number. Cannot be used with label.

Returns:
    A dictionary containing the raw prompt details with dependency tags preserved.

Raises:
    ValueError: If both label and version are specified
    LookupError: If prompt not found

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesThe name of the prompt to fetch
labelNoLabel to fetch (e.g., 'production', 'staging'). Mutually exclusive with version.
versionNoSpecific version number to fetch. Mutually exclusive with label.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description fully explains behavior: returns raw prompt with dependency tags intact, notes fallback when SDK doesn't support resolve=false (returns resolved and sets metadata.resolved=True), and lists exceptions raised (ValueError, LookupError). This provides good insight into the tool's operation.

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?

Description is well-structured with a clear first sentence stating purpose, followed by behavioral explanation, then structured Args/Returns/Raises sections. It is concise and front-loaded with the key differentiator.

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 output schema exists, description doesn't need to detail return values. It covers behavior, failure modes, and parameter constraints comprehensively for a tool of this complexity.

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?

Schema coverage is 100%, so baseline is 3. The description adds minor reinforcement of mutual exclusivity between label and version, but doesn't provide significant meaning beyond the schema. The Args section largely repeats schema content.

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?

Description clearly states 'Fetch a specific prompt by name WITHOUT resolving dependencies.' It specifies the verb (fetch), resource (prompt by name), and the key differentiator (unresolved) which distinguishes it from sibling tools like get_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?

Description states 'Useful for analyzing prompt composition and debugging dependency chains,' clearly implying use cases. While it doesn't explicitly list when not to use, the sibling 'get_prompt' is naturally understood as the alternative for resolved 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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