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avivsinai

langfuse-mcp

get_session_details

Retrieve detailed session information from Langfuse observability, including observations with system prompts and model parameters, in compact or full JSON formats.

Instructions

Get detailed information about a specific session.

Args:
    ctx: Context object containing lifespan context with Langfuse client
    session_id: The ID of the session to retrieve (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:
    Based on output_mode:
    - compact: Summarized session details object
    - full_json_string: String containing the full JSON response
    - full_json_file: Summarized session details object with file save info

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
session_idYesThe ID of the session to retrieve (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 explains performance implications ('Significantly increases response time'), output behavior based on 'output_mode', and file-saving actions for 'full_json_file'. It covers key behavioral traits like response format variations and performance trade-offs.

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 clear sections (Args, Returns, Usage Tips) and front-loaded purpose. Every sentence adds value, though the 'Usage Tips' section could be slightly more concise. The information density is high with minimal redundancy.

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 with nuanced interactions), no annotations, and an output schema present, the description is complete. It explains parameter semantics, behavioral implications, usage scenarios, and output variations. The presence of an output schema means return values don't need explanation in the description.

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 description coverage is 100%, so the baseline is 3. The description adds meaningful context beyond the schema: it explains the practical impact of 'include_observations' ('when you need access to system prompts, model parameters'), pairs parameters in 'Usage Tips', and clarifies the 'ctx' parameter's purpose ('containing lifespan context with Langfuse client'). This provides valuable semantic understanding.

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 with a specific verb ('Get detailed information') and resource ('about a specific session'), distinguishing it from sibling tools like 'fetch_sessions' (which likely lists multiple sessions) and 'get_user_sessions' (which filters by user). The opening sentence directly answers what the tool does without restating the name.

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 different parameter combinations, including specific scenarios like 'quick browsing', 'full data but viewable in responses', and 'complete data dumps'. It offers clear alternatives within the tool's parameter space, though it doesn't explicitly compare to sibling tools.

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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