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dmf_export_package

Trigger a bulk export through the Data Management package REST API and return the download URL when complete. NOTE: the DMF EXPORT project (definitionGroupId) must already exist in FO with its entities + mapping configured -- create it with dmf_create_data_project if needed. For ad-hoc exports without a project, use odata_export_entity instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reExecuteNoRe-execute the project (true) or reuse last staging (false). Default true.
legalEntityYesLegal entity / company id to export from, e.g. 'USMF'.
packageNameNoOptional package name. Default derived from the project id.
definitionGroupIdYesExisting DMF export project (definitionGroupId) in FO.
pollTimeoutSecondsNoMax seconds to poll for completion. Default 300.

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the behavioral burden. It discloses that the tool triggers an export, waits for completion, and then returns a download URL, which communicates the asynchronous/polling nature. It does not discuss side effects like staging reuse or failure behavior, but the main operational behavior is clear.

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 compact and front-loaded with the primary action, then adds a prerequisite note and a clear alternative. Every sentence earns its place, and there is no redundant repetition of schema information.

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 gives the core output (download URL), the prerequisite project setup, and the routing alternative, which is strong for a 5-parameter tool with no output schema. It could additionally note failure/timeout outcomes, but the parameter schema already covers pollTimeoutSeconds, so the overall picture is sufficiently complete.

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%, so all five parameters are already documented with meaningful descriptions. The tool description adds high-level context about definitionGroupId being an existing DMF project, but does not need to repeat parameter details because the schema already handles them.

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 triggers a bulk export via the Data Management package REST API and returns a download URL when complete. It distinguishes itself from related tools by emphasizing the project-based export path and explicitly naming odata_export_entity as the alternative for ad-hoc exports.

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?

The description gives explicit when-to-use guidance: use this when a DMF export project already exists. It also says to create the project with dmf_create_data_project if needed, and to use odata_export_entity for ad-hoc exports without a project. This leaves little ambiguity about tool selection.

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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TDQS

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes and clear triggers, reducing ambiguity. For example, PR-related tools are separated into analysis, listing, commenting, and dependency mapping. However, some overlap exists between find_references, find_extensions, and find_callers, which could confuse an agent without careful descriptions.

Naming Consistency4/5

Tool names follow a consistent snake_case pattern with verb_noun structure within subgroups (e.g., ado_*, find_*, search_*, generate_*). There is no mixing of camelCase or other styles, though the variety of prefixes slightly reduces predictability.

Tool Count3/5

With 38 tools, the server feels slightly over-scoped for its domain. While each tool has a specific function, the number is high compared to typical well-scoped servers (10-15 tools). Some tools like find_references and find_callers could be consolidated.

Completeness4/5

The tool set covers a broad range of D365 F&O development and DevOps tasks, including code search, analysis, security, performance, upgrades, and work item management. Minor gaps exist, such as the absence of direct object modification or batch job management, but the core workflows are well covered.