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list_search_history

Retrieve past searches from a deepset workspace to analyze user queries, answers, and feedback for debugging or usage insights.

Instructions

Retrieves search history for the configured deepset workspace.

Returns past searches run in the workspace, including queries, answers, prompts, feedback, and metadata. Use this to inspect what users have searched for, analyze usage, or debug pipeline behavior. :param limit: Maximum number of entries to return per page. :param after: The cursor to fetch the next page of results. If there are more results to fetch, the cursor will appear as next_cursor on the response. :param query_filter: An OData filter expression to narrow down results. Supported fields: query, client_source_path, pipeline_version_id, answer, api_key, created_at, created_by, tags/tag_id, feedbacks, feedbacks/score, feedbacks/comment, feedbacks/bookmarked, session_id, search_session_id, feedbacks/result_id, request/filters, request/params, duration. Example: "created_at ge 2024-01-01T00:00:00Z" or "query eq 'my search'". :returns: Paginated list of search history entries or 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
limitNo
afterNo
query_filterNo
Behavior4/5

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns past searches, that the output is automatically stored and can be referenced, and that a formatted preview with an object ID is returned. It does not explicitly state read-only behavior, but the nature of retrieving history implies non-destructive operation. This is sufficient transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured: a one-sentence summary, then what it returns and use cases, parameter details, and notes on object storage. Every sentence adds value, and there is no redundancy or filler.

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 that there is no output schema, the description thoroughly explains the return value (paginated list, object ID, formatted preview) and how to use the object ID with other tools. It also covers parameter semantics. This provides complete context for an agent to use the tool correctly.

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?

The input schema has 0% description coverage, but the description provides full semantic details for all three parameters: limit (page size), after (cursor for pagination), and query_filter (OData filter with supported fields and examples). This exceeds the baseline requirement.

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 'Retrieves search history for the configured deepset workspace' and enumerates the contents (queries, answers, prompts, etc.). It uses specific verb and resource, distinguishing it from sibling 'list_pipeline_search_history' by specifying workspace-level scope.

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 use cases: 'inspect what users have searched for, analyze usage, or debug pipeline behavior.' While it doesn't mention when not to use it or name alternatives, the context is clear enough for an AI agent to decide.

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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