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ALM X++ MCP Server

by io.github.alimbenhelal-pro

list_objects

Read-onlyIdempotent

WHEN: you need ALL objects of a given type or in a given model. Triggers: 'list all tables in ALM', 'show all classes', 'quels objets dans le modèle', 'give me all forms'. Full index scan -- returns EVERY matching object, not just top search results. Use to discover what tables, classes, forms, enums, etc. exist in a specific model. When no filters are given and a custom model is configured, defaults to listing that model. NOT for a single object -- use get_object_details. NOT for natural language search -- use search_d365_code.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
aotTypeNoFilter by AOT type: AxClass, AxTable, AxForm, AxView, AxEnum, AxEdt, AxDataEntityView, AxSecurityPrivilege, AxSecurityDuty, AxSecurityRole, AxQuery, AxReport, Documentation. Leave empty for all types.
modelNameNoFilter: matches BOTH package/module name (partial) AND object name (substring). Examples: 'ALM' (package), 'Cust' (any object containing 'Cust'), 'SalesTable'. Leave empty for all.

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already establish read-only and idempotent behavior. The description adds useful operational context: it performs a full index scan, returns every match rather than top results, and defaults to the configured custom model when no filters are provided.

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 a clear WHEN/triggers/scope/not format and is front-loaded. Minor redundancy exists between 'need ALL objects' and the later 'Use to discover' sentence, but overall it is efficient and readable.

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 tool is simple with two optional parameters, and the description covers scope, defaults, behavior, and exclusions. It does not describe the exact structure of returned objects, but 'returns EVERY matching object' gives enough expectation for a discovery tool without an output schema.

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 already documents both parameters thoroughly. The description repeats the filter semantics but adds only the default-model behavior, which is useful but does not substantially extend parameter understanding.

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 the exact purpose: list ALL objects of a given type or model. It provides concrete trigger phrases and explicitly distinguishes itself from get_object_details and search_d365_code, so an agent can clearly identify when this tool is appropriate.

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?

Usage guidance is explicit: use when a full inventory of object types or model contents is needed, and NOT for single objects or natural language search. It names the sibling tools to use instead, leaving no ambiguity.

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