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Glama

ALM X++ MCP Server

by io.github.alimbenhelal-pro

fix_best_practice_violations

Read-onlyIdempotent

WHEN: validate_best_practices returned violations and you want concrete X++ fix suggestions. Returns each BP violation with its built-in fix suggestion AND (when Ollama is available) an AI-generated X++ code patch using the source from the knowledge base. Triggers: 'fix best practice', 'corriger les violations', 'generate fix', 'patch BP'. Set generatePatches=true to call Ollama (ALMXPP_SUMMARIZE_MODEL) for code patches -- requires Ollama running at OLLAMA_HOST. Falls back to built-in fix suggestions if unavailable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
objectNameYesObject or method to fix, e.g. 'SalesLine' or 'SalesLine.insert'.
maxViolationsNoMax violations to process (1-20, default 10).
generatePatchesNoGenerate X++ code patches via Ollama (requires OLLAMA_HOST). Default false.

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description reinforces this by saying the tool 'returns' suggestions and patches rather than applying changes. It openly discloses the external Ollama dependency, the required OLLAMA_HOST, the model name, and the fallback behavior when Ollama is unavailable.

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 primary use case. Trigger phrases add a bit of length but help with selection. It remains information-dense without being bloated.

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 moderate-complexity tool with annotations and full schema coverage, the description covers the key context: triggering condition, return content, Ollama dependency, and fallback. It does not detail the exact response structure, but the described output shape is probably sufficient without an output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema documents all parameters. The description adds meaningful semantics beyond the schema, especially for generatePatches, by naming the model (ALMXPP_SUMMARIZE_MODEL), the Ollama requirement, and the fallback to built-in suggestions. Other parameters are adequately covered by 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 states a specific purpose: return BP violations with fix suggestions and AI-generated X++ patches. It explicitly frames the tool as a follow-up to validate_best_practices, which distinguishes it clearly from the closest sibling and from other fix/suggestion tools.

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 description gives a clear precondition ('WHEN: validate_best_practices returned violations') and explains when Ollama should be used via generatePatches. It does not explicitly list when not to use the tool or name alternatives beyond validate_best_practices, but the context is strong.

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.

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