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deals_list_all_auto

Fetch all deals from Pipedrive in one response with automatic pagination. Filter by status, pipeline, stage, or date to narrow results.

Instructions

Automatically fetch ALL deals with pagination handling.

This tool automatically handles pagination and fetches all deals matching the filters. Unlike deals/list, this returns ALL results in a single response.

WARNING: This can return large datasets. Use filters to limit results.

Workflow tips:

  • Same filters as deals/list (status, stage_id, user_id, person_id, org_id, etc.)

  • Specify max_items to limit total results if needed

  • No need to manage start/limit - pagination is automatic

  • Best for exports, reports, or comprehensive analysis

  • Use add_time_from / add_time_until to filter by creation date (client-side)

Common use cases:

  • Get all open deals: { "status": "open" }

  • Export all deals for a pipeline: { "pipeline_id": 1 }

  • Get old unqualified deals: { "pipeline_id": 4, "add_time_until": "2023-12-31" }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoField to sort by
org_idNoFilter by organization ID
statusNoFilter by deal status
sort_byNoSort direction
user_idNoFilter by user (owner) ID
stage_idNoFilter by stage ID
filter_idNoID of the filter to use
max_itemsNoMaximum number of items to return
person_idNoFilter by person ID
pipeline_idNoFilter by pipeline ID
owned_by_youNoFilter deals owned by the authorized user
add_time_fromNoFilter deals created on or after this date (YYYY-MM-DD). Applied client-side.
add_time_untilNoFilter deals created on or before this date (YYYY-MM-DD). Applied client-side.
Behavior3/5

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

No annotations are provided, so the description must disclose behavioral traits. It warns about large datasets, explains automatic pagination, and notes that add_time_from/until are client-side. However, it does not mention rate limits, authentication needs, or potential side effects beyond data size.

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 sections for warning, workflow tips, and common use cases. It is front-loaded with the main purpose. However, the workflow tips section could be slightly more concise, but overall it is efficient.

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

Completeness3/5

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

For a complex tool with 13 parameters and no output schema, the description covers pagination, filtering, and use cases. However, it lacks information about the response format or structure, which would be helpful for agents to process the results.

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 100%, so baseline is 3. The description adds value by explaining that filters are the same as deals/list, recommending max_items to limit results, and clarifying that date filters are applied client-side. Examples further enhance 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 'Automatically fetch ALL deals with pagination handling' and distinguishes from sibling 'deals/list' by noting that this tool returns all results in a single response. The verb 'fetch' and resource 'deals' are specific, and the automatic pagination feature is highlighted.

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 includes when to use ('Best for exports, reports, or comprehensive analysis'), warns about large datasets, provides common use cases with example filters, and implies not to use for simple queries (use deals/list). It does not explicitly state when not to use, but the context makes it clear.

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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