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avivsinai

langfuse-mcp

get_prompt_unresolved

Fetch raw prompt content with dependency tags preserved for analyzing prompt composition and debugging dependency chains in Langfuse.

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 provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it returns raw content with dependency tags intact (or resolved content if SDK doesn't support it), marks metadata.resolved status, and raises specific errors (ValueError, LookupError). It also explains the fallback behavior when resolve=false is unsupported. However, it doesn't mention rate limits, authentication needs, or side effects, leaving some gaps.

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 clear sections (purpose, behavior, args, returns, raises) and front-loaded key information. It avoids redundancy, though the 'Args' section partially repeats schema details. Every sentence adds value, such as explaining the SDK fallback and usage context. It could be slightly more concise by integrating the 'Args' details more seamlessly, but overall it's efficient.

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's complexity (fetching with dependency handling), no annotations, and an output schema (implied by 'Returns' section), the description is complete enough. It covers purpose, usage, behavior, parameters, return values, and error conditions. The output schema existence means the description doesn't need to detail return structure, and it adequately addresses the tool's unique aspects (unresolved vs. resolved behavior). No critical information is missing for effective use.

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 description coverage is 100%, so the schema already documents all parameters (name, label, version) with their descriptions and constraints (e.g., mutual exclusivity). The description repeats some of this information in the 'Args' section but doesn't add significant meaning beyond what's in the schema. It does clarify the purpose of 'label' with examples ('production', 'staging'), but this is minimal added value. The baseline of 3 is appropriate given high schema coverage.

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's purpose: 'Fetch a specific prompt by name WITHOUT resolving dependencies.' It specifies the verb ('fetch'), resource ('prompt'), and key distinguishing feature ('without resolving dependencies'), which differentiates it from sibling tools like 'get_prompt' that likely resolve dependencies. The description explicitly contrasts with resolved behavior, making the purpose highly specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidance: 'Useful for analyzing prompt composition and debugging dependency chains.' This tells the agent when to use this tool (for analysis/debugging) versus alternatives that resolve dependencies. It also mentions the SDK condition ('when the SDK supports resolve=false'), offering context on applicability. The guidance is clear and actionable.

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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