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get_pipeline_trace_logs

Retrieve log entries from a Haystack pipeline run trace to diagnose warnings or errors. Input pipeline name and query ID from list_pipeline_traces.

Instructions

Retrieves the log entries for a single Haystack pipeline run trace.

Returns just the run's logs — a cheaper, targeted alternative to get_pipeline_trace when only the logs are needed (e.g. to diagnose warnings or errors emitted during the run). Each entry includes the logger, level, message, timestamp, and extra fields.

Obtain query_id from list_pipeline_traces or list_pipeline_search_history. :param pipeline_name: Name of the pipeline. :param query_id: UUID of the search history query. :returns: List of log entries or an error message.

The output is automatically stored and can be referenced in other functions. Returns a formatted preview with an object ID (e.g., @obj_123). Use the object store tools in combination with the object ID to view nested properties of the object. Use the returned object ID to pass this result to other functions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
query_idYes
pipeline_nameYes
Behavior4/5

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

With no annotations provided, the description carries full burden and explains return structure (log fields, object ID), object store referencing, and automatic storage. However, it does not mention potential side effects, authentication, or error handling details beyond an error message.

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 somewhat lengthy but front-loads key purpose and usage. All sentences add value, though there is slight redundancy (e.g., mentioning 'logs' multiple times). Overall efficient.

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 no output schema, the description fully explains return contents and object store integration. It covers input sources and provides clear guidance for an agent to use the tool correctly, with no 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 0%, but description adds meaning by stating pipeline_name is the pipeline name and query_id is a UUID from search history queries. The parameter descriptions are brief but sufficient for usage.

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 retrieves log entries for a pipeline run trace, and distinguishes it from the sibling get_pipeline_trace by noting it is a 'cheaper, targeted alternative' for when only logs are needed.

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?

Explicitly says to use this tool for diagnosing warnings or errors, provides a when-not-to-use alternative (get_pipeline_trace), and instructs how to obtain the required query_id from list_pipeline_traces or list_pipeline_search_history.

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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