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recommend_extension_strategy

Read-onlyIdempotent

WHEN: developer about to customise a standard D365 object asks 'should I use AxTableExtension, Chain of Command, EventHandler, or Delegate?'. Triggers: 'how to extend', 'best way to customise', 'extension strategy for', 'CoC ou event handler', 'comment etendre X'. Returns a ranked recommendation based on the target AOT type and the intent, with citations to the Microsoft Learn extensibility guide. Cloud-safe: pure KB lookup, no writes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
intentYesPlain-language goal of the customisation. Examples: 'add a status field', 'change validateWrite logic', 'react to record insert', 'replace the posting logic', 'override the find method', 'extend the enum'.
objectNameYesTarget standard D365 object name, e.g. 'SalesTable', 'CustTable', 'SalesLineType', 'PurchFormLetter_Confirmation'.

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already provide readOnlyHint and idempotentHint; the description adds value by confirming 'Cloud-safe: pure KB lookup, no writes' in plain language and disclosing that results include ranked output with citations to Microsoft Learn. This enriches the behavioral profile beyond the boolean hints without contradicting them.

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 compact and front-loaded with the WHEN condition, followed by triggers, return behavior, and safety note. The trigger list is slightly verbose but earns its place by helping an agent recognize invocation intent from varied phrasings. No filler.

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?

For a two-parameter, no-output-schema KB lookup, the description is nearly complete: it states inputs, output type (ranked recommendation with citations), and safety profile. The only minor gap is that it doesn't describe the shape of the ranking or how citations are presented, but given the tool's simplicity and the annotations, this is sufficient.

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%, with both parameters fully described and supplied with realistic examples. The description adds contextual mapping ('target AOT type' ↔ objectName, 'intent' ↔ intent), but since the schema already carries the semantic load, 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 names a specific verb and resource: it returns a ranked extension-strategy recommendation for D365 objects. It names the exact decision it resolves (AxTableExtension vs Chain of Command vs EventHandler vs Delegate) and clearly differentiates from siblings like find_extensions, which locates existing extensions rather than recommending a strategy.

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 WHEN condition is explicit and actionable: 'developer about to customise a standard D365 object asks...' plus concrete trigger phrases ('how to extend', 'best way to customise', 'CoC ou event handler'). It does not name sibling alternatives or state explicit non-use conditions, but the trigger guidance is specific enough that an agent can reliably select it.

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.