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Pega DX MCP Server

by marco-looy

get_data_view_count

Retrieve the total record count for a data view query to plan pagination and optimize performance before fetching full data.

Instructions

Retrieve the total count of results for a specified data view query without fetching the actual data. This is useful for pagination planning, understanding dataset sizes, and performance optimization before executing full data retrieval operations.

Supports the same comprehensive query capabilities as get_list_data_view:

  1. Simple Count: Get total count of all records in a data view Example: { "dataViewID": "D_Employees" }

  2. Count with Parameters: Count records with data view parameters for parameterized data views Example: { "dataViewID": "D_CustomerOrders", "dataViewParameters": { "CustomerID": "C-123", "Status": "Active" } }

  3. Filtered Count: Count records matching specific filter criteria Example: { "dataViewID": "D_Employees", "query": { "filter": { "filterConditions": { "F1": { "lhs": {"field": "Department"}, "comparator": "EQ", "rhs": {"value": "IT"} } }, "logic": "F1" } } }

  4. Distinct Count: Count unique combinations of selected fields Example: { "dataViewID": "D_Employees", "query": { "select": [{"field": "Department"}], "distinctResultsOnly": true } }

  5. Aggregated Count: Count records with aggregation grouping Example: { "dataViewID": "D_Sales", "query": { "aggregations": { "TotalRevenue": { "field": "Revenue", "summaryFunction": "SUM" } }, "select": [{"aggregation": "TotalRevenue"}] } }

Filter comparators supported: boolean (IS_TRUE, IS_FALSE, IS_NULL, IS_NOT_NULL, EQ, NEQ), string (EQ, NEQ, IN, NOT_IN, IS_NULL, IS_NOT_NULL, STARTS_WITH, NOT_STARTS_WITH, ENDS_WITH, NOT_ENDS_WITH, CONTAINS, NOT_CONTAINS), number/date (EQ, NEQ, IN, NOT_IN, GT, GTE, LT, LTE, ISNULL, ISNOTNULL).

Aggregation functions: COUNT, MAX, MIN, DISTINCT_COUNT. For numbers: SUM, AVG.

Calculation functions: YEARS, QUARTERS, MONTHS, WEEKS, DAYS, HOURS, MONTHS_OF_YEAR, DAYS_OF_MONTH, DAYS_OF_WEEK, INTERVAL_GROUPING_FLOOR, INTERVAL_GROUPING_CEILING.

Note: Maximum result count is 5000 for queryable data views. The hasMoreResults field indicates if there are additional results beyond the count limit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataViewIDYesData view ID. Example: "D_CaseList"
dataViewParametersNoParameters for parameterized data views. Key-value pairs. Example: {"CustomerID": "C-123"}
queryNoOptional query configuration for filtering, aggregation, and field selection. Uses the same structure as get_list_data_view for consistency.
pagingNoOptional pagination configuration that affects count calculation. Can specify either maxResultsToFetch or pageNumber/pageSize combination, but not both.
sessionCredentialsNoOptional session-specific credentials. If not provided, uses environment variables. Supports two authentication modes: (1) OAuth mode - provide baseUrl, clientId, and clientSecret, or (2) Token mode - provide baseUrl and accessToken.
Behavior3/5

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

No annotations provided; description covers max results limit and hasMoreResults field, but does not explicitly state read-only nature or authentication implications beyond parameter descriptions.

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?

Well-structured with numbered examples and clear sections. Slightly verbose due to many examples, but justified given the tool's complexity.

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?

No output schema; description mentions hasMoreResults but does not specify the exact return structure (e.g., count field name, type). Lacks details on response format for success/error.

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 100%, but description adds significant value with extensive examples for all query configurations, covering aggregation, filter, calculation, and paging 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 a total count without fetching data, and distinguishes from get_list_data_view. Examples reinforce purpose.

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?

Explicitly mentions use cases like pagination planning and performance optimization. Implicitly contrasts with get_list_data_view via 'same comprehensive query capabilities,' but does not explicitly state when not to use.

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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