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create_comment

Add a markdown comment to a trace, observation, session, or prompt in Langfuse to provide context or feedback directly on the object.

Instructions

Create a comment on a trace, observation, session, or prompt.

project_id: the Langfuse project ID (different from the MCP project argument — find it in your Langfuse dashboard URL). object_type: 'trace' | 'observation' | 'session' | 'prompt'. content: markdown body, up to 5000 characters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYes
projectNo
object_idYes
project_idYes
object_typeYes
author_user_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not describe side effects, authorization requirements, reversibility, or response behavior beyond the implicit fact that a comment is created. The note about project_id and content constraints adds parameter detail, not behavioral transparency.

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 concise and front-loaded with the primary purpose. Parameter details are presented in a structured list, though line breaks are used somewhat informally. Every sentence adds relevant information without fluff.

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

Completeness3/5

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

The tool has an output schema, so return values are covered. However, with 6 parameters, zero annotation coverage, and no usage guidelines, the description is not fully complete. It provides some parameter constraints but leaves gaps in behavioral context and alternative usage scenarios.

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 0%, so the description must compensate. It explains project_id (distinct from MCP project argument), object_type (with enum values), and content (markdown, 5000 char limit). However, object_id, author_user_id, and project remain undocumented in the description, leaving three of six parameters semantically uncovered.

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 action and resource: 'Create a comment on a trace, observation, session, or prompt.' This specific verb+resource structure distinguishes it from related sibling tools like get_comment and list_comments by emphasizing the creation aspect.

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?

The intended use (to create a comment) is implied, but the description does not explicitly state when to use this tool over alternatives or provide exclusion criteria. Sibling tools like get_comment and list_comments exist, but no cross-referencing is provided.

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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