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avivsinai

langfuse-mcp

create_text_prompt

Create new text prompt versions in Langfuse for LLM applications. Manage immutable prompts with variables, labels, and configuration settings.

Instructions

Create a new text prompt version in Langfuse.

Prompts are immutable; creating a new version is the only way to update prompt content. Labels are unique across versions - assigning a label here will move it from other versions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesThe name of the prompt to create
promptYesPrompt text content (supports {{variables}})
labelsNoLabels to assign (e.g., ['production', 'staging'])
configNoOptional JSON config (e.g., {model: 'gpt-4', temperature: 0.7})
tagsNoOptional tags for organization (e.g., ['experimental', 'v2'])
commit_messageNoOptional commit message describing the changes

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 behavioral traits: the immutability of prompts (which implies this is a write operation), the versioning mechanism, and the unique label behavior that moves labels from other versions. It doesn't mention permissions, rate limits, or error conditions, but covers the essential mutation behavior well.

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 perfectly concise with only three sentences that each earn their place: the core action, the immutability constraint, and the label uniqueness behavior. It's front-loaded with the primary purpose and wastes no words.

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 that this is a mutation tool with no annotations but with a complete input schema and an output schema (which exists according to context signals), the description provides good contextual completeness. It explains the essential behavioral constraints (immutability, versioning, label uniqueness) that aren't captured in structured fields. The output schema will handle return values, so the description doesn't need to cover those.

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 6 parameters thoroughly. The description adds no specific parameter information beyond what's in the schema. The baseline score of 3 is appropriate when the schema does all the parameter documentation work.

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 ('Create a new text prompt version') and resource ('in Langfuse'), distinguishing it from siblings like 'create_chat_prompt' by specifying 'text prompt' and from 'update_prompt_labels' by explaining that creation is the only way to update content due to immutability.

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 guidance on when to use this tool: 'Prompts are immutable; creating a new version is the only way to update prompt content.' It also distinguishes it from alternatives by explaining that labels are unique across versions, which helps differentiate from sibling tools like 'update_prompt_labels'.

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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