dataverse-mcp-server

create_record

Adds a new row to any Microsoft Dataverse table by supplying the entity set name and field values as key-value pairs.

Instructions

Create a new record in a Dataverse table

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": {
      "entity_set": "<entity_set>",
      "data": "<data>"
    },
    "name": "create_record"
  }
}

Input Schema

Name	Required	Description	Default
`data`	Yes	Record fields as key-value pairs
`entity_set`	Yes	Entity set name (plural, e.g. 'accounts', 'contacts')

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv0.9.0Oct 3, 2026

changedInput schema / $schema

Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"

First observedv0.5.0Jul 31, 2026

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations, the description carries the full behavioral burden, yet it discloses nothing about side effects, required permissions, whether the call is idempotent, or what happens on duplicate/conflicting data. For a mutation tool this is a significant gap, though 'Create a new record' at least correctly signals a write rather than a read.

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?

A single short sentence with no waste and the verb front-loaded. It is efficient, but the brevity is partly under-specification rather than disciplined conciseness, so it does not reach a 5.

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?

This is a mutation tool with no annotations and no output schema, and the description says nothing about return values, error behavior, or permission requirements. The fully-covered input schema compensates for parameter documentation but not for the behavioral context an agent needs before performing a write.

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 description coverage is 100%: both entity_set (plural entity set name with examples) and data (key-value record fields) are documented in the schema itself. The description adds no syntax, format, or constraint detail beyond that, so the baseline 3 applies.

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?

States a specific verb (Create) and resource (a new record in a Dataverse table), so an agent immediately knows this is the write counterpart to get_record/update_record/delete_record. It stops short of explicitly naming a sibling or distinguishing its scope from create_entity, which operates on table metadata rather than rows.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus the many siblings (update_record, create_entity, query_records), nor any stated prerequisites such as required permissions or the need for a valid entity_set. The agent must infer usage entirely from the name.

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