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ALM X++ MCP Server

by io.github.alimbenhelal-pro

suggest_edt

Read-only

WHEN: adding a new field to a table -- find the best existing D365 EDT to extend instead of using raw primitives (str, int64, real, date). Triggers: 'what EDT for', 'which EDT should I extend', 'quel EDT pour', 'quel type étendu', 'EDT for a field'. D365 best practice mandates EDT reuse over raw primitive types. Call BEFORE declaring any field with a primitive type. Returns ranked candidate EDTs with their base type, label, and model.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topKNoNumber of EDT candidates to return (default: 8, max: 20)
purposeYesPurpose of the field in plain language, e.g. 'customer account number', 'approval status enum', 'invoice amount in transaction currency'
baseTypeNoOptional: D365 primitive base type to filter by, e.g. 'str', 'int64', 'real', 'date', 'enum'. Leave empty to search all types.
fieldNameYesField name or concept, e.g. 'AccountNum', 'vendorId', 'itemCode', 'approvalStatus'

TDQS

A4.3/5.0
Behavior4/5

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

Annotation readOnlyHint=true already covers safety; the description adds the D365 best-practice rationale and the exact shape of the returned data (ranked EDTs with base type, label, model). 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with 'WHEN', includes compact trigger examples, and every sentence earns its place—usage, rationale, directive, and return value. No filler or repetition.

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?

With no output schema, the description still states what the tool returns (ranked candidates and their attributes). Together with 100% schema coverage and readOnlyHint, an agent has everything needed to decide when to call it and what to do with the result.

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 the schema carries the parameter documentation burden. The description's field examples reinforce purpose but add minimal information beyond the schema.

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 uses a specific verb phrase ('find the best existing D365 EDT to extend') plus the concrete context of adding a field and the return of ranked candidates. It clearly differentiates the tool from generic refactoring/best-practice siblings like suggest_refactoring or fix_best_practice_violations.

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?

It gives explicit WHEN conditions, trigger phrases in multiple languages, and a strong directive ('Call BEFORE declaring any field with a primitive type'). It doesn't name a sibling alternative or state when not to call it, but the context is unambiguous.

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.

Resources