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explain_workflow

Read-only

WHEN: user asks how an approval workflow works, who approves a document, what states it goes through, or what happens on submission/rejection. NOT for technical workflow class details -- use get_object_details.

WORKFLOW EXPLAINER (Business Language) -- Explains a D365 approval workflow: who approves, what states exist, and what happens on approval or rejection. Output is plain business language -- no X++ or workflow engine jargon.

Triggers: 'explain the workflow for', 'how does the approval work', 'qui approuve', 'workflow states for', 'étapes du workflow', 'approval process for', 'circuit d'approbation', 'what happens when a user submits'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
objectNameYesD365 object or workflow name, e.g. 'SalesTable', 'PurchTable', 'ALMDemandeAchatWorkflow'

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true. The description adds meaningful behavioral context beyond that: output is plain business language, no X++ or workflow engine jargon, and it covers approval/rejection outcomes. This helps set expectations for the response style.

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?

The description is well-structured with WHEN, NOT, and trigger sections, and the key scoping information is front-loaded. The trigger list is slightly redundant with the opening WHEN clause, but it adds concrete value for intent matching.

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?

For a read-only, single-parameter tool with no output schema, the description provides everything an agent needs: when to use it, what it explains, the output language style, and the exclusion boundary. No critical context is missing.

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%, and the parameter description already provides examples like 'SalesTable' and 'ALMDemandeAchatWorkflow'. The tool description does not add new parameter-level meaning, but with full schema coverage the 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 states a specific verb-resource pair: explains a D365 approval workflow, covering who approves, states, and outcomes. It clearly distinguishes itself from get_object_details by explicitly excluding technical workflow class details.

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?

Provides explicit WHEN conditions with concrete user-question examples, and an explicit NOT condition with the alternative tool to use (get_object_details). Trigger phrases, including multilingual examples, leave no ambiguity about when to invoke this tool.

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.