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avivsinai

langfuse-mcp

fetch_trace

Retrieve detailed trace data by ID from Langfuse to debug LLM applications, with options for observation inclusion and output formats.

Instructions

Get a single trace by ID with full details.

Args:
    ctx: Context object containing lifespan context with Langfuse client
    trace_id: The ID of the trace to fetch (unique identifier string)
    include_observations: If True, fetch and include the full observation objects instead of just IDs.
        Use this when you need access to system prompts, model parameters, or other details stored
        within observations. Significantly increases response time but provides complete data.
    output_mode: Controls the output format and detail level

Returns:
    One of the following based on output_mode:
    - For 'compact' and 'full_json_file': A response dictionary with the structure:
      {
          "data": Single trace object,
          "metadata": {
              "file_path": Path to saved file (only for full_json_file mode),
              "file_info": File save details (only for full_json_file mode)
          }
      }
    - For 'full_json_string': A string containing the full JSON response

Usage Tips:
    - For quick browsing: use include_observations=False with output_mode="compact"
    - For full data but viewable in responses: use include_observations=True with output_mode="compact"
    - For complete data dumps: use include_observations=True with output_mode="full_json_file"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
trace_idYesThe ID of the trace to fetch (unique identifier string)
include_observationsNoIf True, fetch and include the full observation objects instead of just IDs. Use this when you need access to system prompts, model parameters, or other details stored within observations. Significantly increases response time but provides complete data. Pairs well with output_mode='full_json_file' for complete dumps.
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
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 performance implications ('Significantly increases response time'), output behavior based on 'output_mode', and file-saving actions for 'full_json_file'. However, it doesn't mention error handling, rate limits, or authentication requirements.

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 well-structured with clear sections (Args, Returns, Usage Tips), front-loading the core purpose. Every sentence adds value: explaining parameters, return formats, and practical usage scenarios without redundancy or fluff.

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 (3 parameters, output variations) and the presence of an output schema (which handles return value documentation), the description is complete. It covers purpose, parameter semantics, behavioral traits, and usage guidelines, leaving no significant gaps for agent understanding.

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?

The schema has 100% description coverage, so the baseline is 3. The description adds value by explaining the practical implications of 'include_observations' ('Use this when you need access to system prompts, model parameters...') and providing usage tips that clarify parameter interactions, though it doesn't add syntax details beyond 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 trace by ID with full details.' It specifies the verb ('Get'), resource ('trace'), and scope ('single trace by ID'), distinguishing it from sibling tools like 'fetch_traces' (plural) and 'fetch_observations' (different resource).

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 'Usage Tips' section provides explicit guidance on when to use specific parameter combinations: 'For quick browsing', 'For full data but viewable in responses', and 'For complete data dumps'. It differentiates use cases based on performance vs. completeness needs, offering clear alternatives within the tool itself.

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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