Dataverse MCP Server

dataverse_count_records

Read-onlyIdempotent

Count records in a table (optionally filtered) and return only the integer total.

Instructions

Count records in a table (optionally filtered) and return only the integer total.

Use this instead of dataverse_query_table when you need a number, not rows. For per-group counts (e.g. count by status) use dataverse_aggregate_table. The total is capped at 5,000 by Dataverse.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "params": "<params>"
    },
    "name": "dataverse_count_records"
  }
}

Input Schema

Name	Required	Description	Default
`params`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv3.6.0Jul 15, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already indicate readOnlyHint, idempotentHint, not destructive. Description adds the 5,000 cap and confirms only integer returned, providing additional behavioral context beyond 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 purpose, no redundant information. Efficiently conveys when to use and key constraint (cap).

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?

With annotations and output schema present, the description is sufficient: it explains the output (integer total) and the cap. No missing details for a simple count tool.

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

Parameters3/5

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

Schema descriptions provide parameter details (e.g., filter expression, URL, entity set name). The tool description does not add parameter semantics, but the schema covers them, so baseline of 3 is appropriate.

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 counts records in a table and returns only the integer total. It distinguishes from dataverse_query_table (returns rows) and dataverse_aggregate_table (per-group counts).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells when to use this tool over siblings: use instead of dataverse_query_table when you need a number, not rows; use dataverse_aggregate_table for per-group counts. Also mentions the 5,000 cap.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server