Skip to main content
Glama

get_pipeline_trace

Obtain the full execution trace for a specific query run, including component inputs, outputs, logs, and timing details, by providing the pipeline name and query ID from list_pipeline_traces.

Instructions

Retrieves the full Haystack pipeline run trace for a single search history record.

Returns the complete execution trace for one query in a single call: every component span with full tags (including the component's input and output), all log entries, timing, and failure details. Use this to deep-dive into a specific query run identified by its query_id (obtainable from list_pipeline_traces or list_pipeline_search_history).

For a targeted look at one component without downloading the whole trace, use get_pipeline_trace_span_tags; for only the logs, use get_pipeline_trace_logs.

This tool resolves the pipeline and workspace IDs automatically and reads the full trace via the v2 trace export endpoint under the hood. :param pipeline_name: Name of the pipeline. :param query_id: UUID of the search history query whose trace to retrieve. Obtain this from the query_id / search_history_id field of a list_pipeline_traces or list_pipeline_search_history response. :returns: The full pipeline trace entry 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
Behavior5/5

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

No annotations are provided, so the description fully carries the burden. It discloses that the tool automatically resolves pipeline and workspace IDs, reads via the v2 trace export endpoint, stores output as an object ID, and gives instructions for further use with object store tools. No behavioral traits are hidden.

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 paragraphs but could be slightly more concise. It front-loads the purpose and uses format elements like colons and line breaks, though it includes some redundancy (e.g., mentioning object ID multiple times).

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 explains the return value (full trace or error message), mentions automatic storage with object ID, and guides use of object store tools. It covers all necessary context for an agent to invoke and understand the result.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description explains both parameters: pipeline_name and query_id, including how to obtain query_id from other functions. This adds meaning beyond the schema, which only provides names and types.

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 'the full Haystack pipeline run trace for a single search history record.' It uses a specific verb ('Retrieves') and resource, and distinguishes itself from sibling tools like get_pipeline_trace_span_tags and get_pipeline_trace_logs by mentioning alternatives.

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 description explicitly says when to use this tool ('deep-dive into a specific query run') and how to obtain the required query_id from list_pipeline_traces or list_pipeline_search_history. It also lists alternative tools for targeted lookups, providing clear usage guidance.

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/deepset-ai/deepset-mcp-server'

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