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LosslessFunction

Dataverse MCP Server

bulk_update_records

Update multiple Dataverse records in a single batch operation. Send record IDs and field changes to apply PATCH updates across up to 1000 records, with per-item success tracking.

Instructions

Update multiple records in a single OData $batch operation. Up to 1000 records per call (processed in chunks of 100). Each item requires a recordId and a data object (PATCH semantics — only provided fields change). Tracks per-record success/failure — partial success is supported.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
itemsYesArray of { recordId, data } objects to update.
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 discloses key behavioral traits: chunking, partial success with per-record tracking, and PATCH semantics. This goes beyond the schema and provides significant context for the agent, though it doesn't describe the exact response format or error behavior.

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 three concise sentences, each containing distinct valuable information: purpose, batch limits, and semantics/error tracking. No redundant wording, and the most important constraint is front-loaded.

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 tool is relatively complex with batch processing and partial success, but the description covers the essential aspects for an agent: limit, chunking, update semantics, and per-record tracking. Lack of an output schema means it could further specify the response shape, but current coverage is strong.

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 coverage is 100%, so the description doesn't need to add parameter-level detail. It does reinforce the requirement of recordId and data objects and clarifies PATCH semantics, but this is largely already present in the schema descriptions. Baseline 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 updates multiple records via a single OData $batch operation, with the verb 'Update' and resource 'records'. It distinguishes itself from sibling bulk_create_records and bulk_delete_records, and from single-record update_record, making the purpose unambiguous.

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 conveys the batch context and constraints (up to 1000 records, chunks of 100, PATCH semantics), which implicitly guides when to use this over single-record updates. However, it does not explicitly mention alternatives or exclusion cases, such as when to use bulk_create_records or batch_transaction instead.

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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