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langfuse-mcp-java

get_prompt

get_prompt
Destructive

Fetch a specific prompt by name from Langfuse, optionally specifying a version number or label like 'production' to retrieve prompt content, configuration, and metadata for LLM application observability.

Instructions

Fetch a specific prompt by name. Optionally pin to a version number or a label (e.g. 'production', 'staging'). Returns: name, version, type (text|chat), prompt content, labels, tags, config. Read-only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesPrompt name (exact match)
versionYesVersion number — omit for latest
labelYesLabel, e.g. 'production' or 'staging'
Behavior1/5

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

The description explicitly states 'Read-only,' which directly contradicts the annotations that specify 'destructiveHint': true and 'readOnlyHint': false. This is a serious inconsistency that could lead an agent to incorrectly assume this is a safe read operation when it may have destructive side effects.

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 compact with four focused sentences covering purpose, parameter semantics, return values, and safety. The structure is logical, though the final 'Read-only' sentence contains erroneous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description compensates for the missing output schema by enumerating return fields (name, version, type, etc.). However, it is incomplete due to contradictions: claiming optional parameters ('Optionally pin') when the schema marks all three as required, and claiming read-only behavior when annotations indicate destructiveness.

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?

While the schema has 100% description coverage, the description adds valuable semantic context by introducing the 'pinning' metaphor for version and label parameters, helping agents understand these are for selecting specific variants. It reinforces the examples (production, staging) provided in the schema.

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 with specific verb ('Fetch') and resource ('prompt'), and specifies the identification method ('by name'). It effectively distinguishes from siblings like create_prompt, delete_prompt, and list_prompts.

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

Usage Guidelines3/5

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

Provides implicit usage guidance by explaining the 'pinning' concept for version/label (e.g., production vs staging), which helps users understand when to use those parameters. However, it lacks explicit guidance on when to use this tool versus list_prompts for discovery or create_prompt for generation.

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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