Skip to main content
Glama
avivsinai

langfuse-mcp

fetch_trace

Retrieve a specific trace by ID with full details. Choose compact or full output, and optionally include observations for complete data on model parameters and prompts.

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. If False, the
        observations field group is dropped entirely to avoid expensive per-observation IO.
        Use True 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)
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
include_observationsNoIf True, fetch and include the full observation objects. If False, the observations field group is dropped entirely to avoid the expensive per-observation IO that dominates large-trace payloads. Use True 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 Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

With no annotations provided, the description fully carries the burden. It explains the performance impact of include_observations, the behavior of dropping observations to avoid IO, and the different output formats. This is comprehensive and beyond minimal.

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 well-structured with Args, Returns, and Usage Tips sections. It is somewhat lengthy but every sentence adds value. Front-loads the purpose effectively.

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 presence of an output schema and rich parameter information, the description is complete. It covers return types, parameter behavior, and use cases without gaps.

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 significant context about performance trade-offs for include_observations and provides usage tips for output_mode, which go beyond the schema descriptions.

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 'Get a single trace by ID with full details.' It uses a specific verb and resource, and it distinguishes itself from siblings like 'fetch_traces' (multiple) and 'fetch_observation' (specific sub-resource).

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

Usage Guidelines4/5

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

The description provides explicit Usage Tips for different combinations of parameters (e.g., 'For quick browsing: use include_observations=False with output_mode="compact"'). It does not explicitly say when not to use this tool vs alternatives, but the context is clear enough for selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/avivsinai/langfuse-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server