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Data Parrot AI Revenue Analyst for HubSpot

Search and Analyze Deals

get_deals
Read-onlyIdempotent

Search, filter, count activity, group, rank, and return open/won/lost CRM deals. Open deals include manual forecast_status and AI forecast_category. Choose one owner selector. For broader AI analysis, inspect deals/analysis_concepts in the catalog.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo
columnsNoOptional Deal fields; omit for the compact default. row_count and amount_sum are grouped metrics.
sort_byNoSort by a catalog-supported Deal field.
deal_idsNo
group_byNo
team_idsNoExclusive selector: exact team IDs, expanded to active owners.
deal_nameNo
owner_idsNoExclusive selector: exact IDs from the owners catalog.
page_sizeNoRow size 1-100; use page/page_size. Groups use group_page_size.
owner_scopeNoExclusive selector: all owners or current authenticated user.
amount_rangeNoInclusive configured Deal-value range.
deal_statusesNo
group_sort_byNoRequired with group_by: row_count, amount_sum, label, or value_order. rows, amount, and revenue are aliases.
company_filterNo
contact_filterNo
sort_directionNoasc
deal_name_matchNocontains
forecast_statusNo
group_page_sizeNo
pipeline_stagesNo
purchase_intentNo
activity_filtersNoUp to 10 ANDed per-deal count filters. Item: {activity_types:[...],count:{operator,value},time_range:{start_date,end_date}}. For no activity use activity_types:["any"], count:{operator:"eq",value:0}, and dates. any counts meetings, calls, sent or received customer emails, and communications; zero includes never-contacted deals.
close_date_rangeNo
custom_propertiesNo
forecast_categoryNo
sales_performanceNo
overall_deal_healthNo
competitive_positionNo
deal_progress_statusNo
group_sort_directionNodesc
close_date_confidenceNo
deal_stage_confidenceNo
deal_probability_rangeNo
custom_property_filtersNo
include_null_close_dateNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYes
kindYes
toolYes
summaryYes
result_idNo
limitationsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already mark this as readOnly, idempotent, and non-destructive, so safety is covered. The description adds the useful behavioral detail that open deals expose manual forecast_status and AI forecast_category, and that owner selection is exclusive. No contradiction with annotations.

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?

Three sentences, front-loaded with the high-level operation and no filler. Each sentence earns its place: scope, owner-selector rule, and catalog pointer.

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

Completeness2/5

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

For a 35-parameter tool with 26% schema coverage, a three-sentence overview leaves out pagination, grouping specifics, date-range semantics, and most filter categories. The output schema helps, but the input schema's low coverage means the description needed to add more operational detail to be complete enough for safe invocation.

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

Parameters2/5

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

Schema description coverage is only 26%, so the description carries a large burden. It names broad capabilities (activity counting, grouping, ranking) and owner selector exclusivity, but it does not explain or enumerate the many undocumented parameters such as pipeline_stages, close_date_range, custom_property_filters, or pagination controls.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a concrete verb set (search, filter, count activity, group, rank) plus the resource (CRM deals) and the statuses returned. It does not explicitly distinguish get_deals from its sibling get_deal, so it falls just short of a top score.

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?

It instructs the agent to choose one owner selector, and it routes broader AI analysis to deals/analysis_concepts in the catalog. It does not state exclusions for singular deal lookup or forecast tools, but the search/analysis framing supplies clear context.

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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