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LosslessFunction

Dataverse MCP Server

bulk_create_records

Create up to 1000 records in one batch operation to Dataverse tables, with per-record success/failure tracking and partial success support.

Instructions

Create multiple records in a single OData $batch operation. Up to 1000 records per call (processed in chunks of 100). Tracks per-record success/failure — partial success is supported.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYesArray of record objects to create.
tableNameYesLogical (schema) name of the Dataverse table, e.g. 'account' or 'crb69_myentity'
Behavior4/5

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

With no annotations, the description carries the burden of behavioral disclosure. It reveals the batch operation, the 1000-record limit, chunk size, and per-record success/failure tracking with partial success support — key traits that affect how the agent should handle results. It does not cover authentication or side effects, but those are less critical for a create operation.

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?

The description is two sentences that front-load the purpose and immediately provide important constraints. Every sentence earns its place, with no fluff or repetition of schema details.

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

Completeness4/5

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

The description addresses the key complexities of a bulk operation: limits, chunking, and partial failure behavior. It does not describe the response format or how to access per-record results, but since there is no output schema, this is less critical. Overall, it is sufficiently complete for an agent to decide whether to invoke this 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?

The input schema provides descriptions for both parameters (tableName and items) with 100% coverage. The description adds no additional parameter-specific meaning beyond 'records' and 'Dataverse table,' which are already in the schema. Baseline 3 is appropriate since the schema does the heavy lifting.

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's function: 'Create multiple records in a single OData $batch operation.' It uses a specific verb ('Create') and resource ('multiple records'), and distinguishes itself from siblings like create_record (single) and bulk_update_records/bulk_delete_records by emphasizing the batch nature.

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?

The description implies usage for bulk creation with explicit constraints (up to 1000 records, chunked in 100) and mentions partial success, which is critical for deciding when to use it. However, it does not explicitly name alternatives or state 'use create_record for a single record,' leaving room for clearer guidance.

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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