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avivsinai

langfuse-mcp

fetch_observation

Retrieve a specific observation by its ID. Choose between compact summary, full JSON string, or saving full data to a file.

Instructions

Get a single observation by ID.

Args:
    ctx: Context object containing lifespan context with Langfuse client
    observation_id: The ID of the observation to fetch (unique identifier string)
    output_mode: Controls the output format and detail level

Returns:
    Based on output_mode:
    - compact: Summarized observation object
    - full_json_string: String containing the full JSON response
    - full_json_file: Summarized observation object with file save info

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
observation_idYesThe ID of the observation to fetch (unique identifier string)
output_modeNoControls the output format and action. 'compact' (default): Returns a summarized JSON object optimized for direct agent consumption. 'full_json_string': Returns the complete, raw JSON data serialized as a string. 'full_json_file': Returns a summarized JSON object AND saves the complete data to a file.compact

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations are provided, so the description must disclose behavioral traits. It describes output modes and return types but does not explicitly state if the operation is read-only, requires permissions, or has any side effects. The output schema covers return values, but behavioral context is lacking.

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 structured with clear sections (Args, Returns) and is appropriately sized for a simple fetch tool. However, it uses docstring formatting that is slightly verbose for a tool description.

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 the simple nature (2 params, output schema exists), the description is fairly complete. It explains the purpose, parameters, and return types for each output mode. Minor gaps include missing prerequisites or error handling, but these are not critical for a basic fetch operation.

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?

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the three output_mode options in detail ('compact' returns summarized JSON, 'full_json_string' returns raw JSON as string, 'full_json_file' returns summarized object and saves file), which goes beyond the enum list 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: 'Get a single observation by ID'. This is a specific verb+resource combination that distinguishes it from sibling tools like 'fetch_observations' (plural) and 'fetch_trace', which operate on different entities.

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 description explains the output_mode parameter but does not explicitly state when to use this tool versus alternatives like 'fetch_observations' (for multiple observations) or other fetch tools. No when-to-use or when-not-to-use guidance 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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