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avivsinai

langfuse-mcp

fetch_traces

Find traces matching filters like name, user, session, metadata, or tags. Specify age for time range and choose output format with optional observation details.

Instructions

Find traces based on filters. All filter parameters are optional.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ageYesMinutes ago to start looking (e.g., 1440 for 24 hours)
nameNoName of the trace to filter by
user_idNoUser ID to filter traces by
session_idNoSession ID to filter traces by
metadataNoMetadata fields to filter by
pageNoPage number for pagination (starts at 1)
limitNoMaximum number of traces to return per page
tagsNoTag or comma-separated list of tags to filter traces by
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.
output_modeNoControls the output format: 'compact' (default) returns summarized JSON, 'full_json_string' returns complete raw JSON as string, 'full_json_file' saves complete data to file and returns summary with path.compact

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations provided, the description bears full responsibility for behavioral traits. It fails to disclose that the tool is read-only, does not mention performance implications of filters like include_observations, and lacks any information about side effects or constraints.

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 a single, concise sentence that is easy to read and front-loaded. However, it could be slightly more structured to include key points like 'Read-only' or 'Returns traces in specified format'.

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

Completeness2/5

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

Despite the presence of an output schema and comprehensive parameter descriptions, the tool's complexity (10 parameters) is not matched by the description. Missing context includes when to use different modes, pagination behavior beyond schema, and typical use cases.

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?

All parameters have detailed schema descriptions (100% coverage), so baseline is 3. The description adds the clarification that all non-required parameters are optional, which is helpful but not extensive.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool finds traces based on filters, which accurately conveys its purpose as a search/query tool. However, it does not explicitly distinguish between this and sibling fetch tools like fetch_trace (single trace) or fetch_sessions, which limits differentiation.

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

Usage Guidelines2/5

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

The description mentions 'All filter parameters are optional', which implies flexibility but offers no guidance on when to use this tool versus alternatives like fetch_trace or fetch_observations. No exclusionary or contextual advice 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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