WHEN: checking server status, loaded D365 version, or custom model path. Triggers: 'status', 'statut', 'is the server ready', 'how many chunks', 'index loaded'. Returns JSON with: status, indexed chunk count, loaded version, custom model path.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint annotation already establishes that this is a safe read operation. The description adds useful behavioral detail by stating that it returns a JSON payload with status, indexed chunk count, loaded version, and custom model path, and by listing trigger word examples. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and front-loaded with the WHEN condition, then trigger phrases, then the output summary. Every sentence contributes value and there is no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a no-parameter, read-only health check tool, the description covers when to call it, how to phrase the request, and what the response contains. The presence of an output schema further covers the return structure, so nothing critical is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. There is no parameter information needed, and the description appropriately focuses on the response rather than input semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool checks server status, loaded D365 version, and custom model path, and it provides concrete trigger phrases. It does not explicitly contrast against sibling tools, so it lacks the final distinguishing nudge, but the purpose is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The WHEN clause and trigger examples ('status', 'statut', 'is the server ready') give an agent explicit conditions for invoking this tool. It does not explain when not to use it or name alternative tools, but the usage context is otherwise very clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.