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pipelines_conversion_statistics

Calculate deal conversion rates between stages and to close for any pipeline over a specified date range. Identify bottlenecks and track sales performance.

Instructions

Get deal conversion rates in a pipeline for a specific time period.

Returns stage-to-stage conversion rates and pipeline-to-close rates, showing how deals progress through the pipeline.

Response includes:

  • stage_conversions: Array of conversion rates between stages

  • won_conversion: Rate of deals won

  • lost_conversion: Rate of deals lost

Workflow tips:

  • Requires start_date and end_date in YYYY-MM-DD format

  • Defaults to authorized user unless user_id is specified

  • Use to analyze pipeline efficiency and identify bottlenecks

  • Track conversion improvements over time

  • Compare different time periods or users

Common use cases:

  • Get Q4 2023 conversions: { "id": 1, "start_date": "2023-10-01", "end_date": "2023-12-31" }

  • Track specific user performance: { "id": 1, "start_date": "2023-01-01", "end_date": "2023-12-31", "user_id": 123 }

  • Identify weak conversion points in pipeline

  • Monitor sales team effectiveness

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesID of the pipeline
start_dateYesStart date in YYYY-MM-DD format (e.g., 2023-01-01)
end_dateYesEnd date in YYYY-MM-DD format (e.g., 2023-12-31)
user_idNoID of user to fetch statistics for (optional, defaults to authorized user)
Behavior4/5

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

The description discloses that it returns conversion rates with specific fields (stage_conversions, won_conversion, lost_conversion) and defaults to the authorized user. It lacks explicit mention of being read-only but is implied. No annotations exist to contradict or supplement.

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 structured into clear sections (purpose, output, workflow tips, use cases) and is front-loaded with the main purpose. Some redundancy exists, but it remains readable and efficiently conveys information.

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 the response structure. All parameters are addressed, and the common use cases illustrate typical invocations. The tool's purpose and context within the pipeline family are well-covered.

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% with descriptions for all parameters. The description adds value by specifying date format (YYYY-MM-DD), the default behavior of user_id, and providing example JSON objects in common use cases.

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 conversion rates for a pipeline over a time period, with specific verb 'Get' and resource 'pipeline'. It differentiates from siblings like 'pipelines_movement_statistics' by focusing on conversion rates rather than movement.

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?

Workflow tips and common use cases provide guidance on when to use the tool, such as analyzing pipeline efficiency and tracking improvements. However, it does not explicitly state when not to use it or mention alternative tools.

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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