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ALM X++ MCP Server

by io.github.alimbenhelal-pro

find_error_patterns

Read-onlyIdempotent

WHEN: a user encounters an error message, infolog error, or runtime exception in D365. Also handles business-language error explanation when audienceType='business'. Triggers (developer): 'fix this error', 'what causes', 'exception thrown', 'infolog error', 'update conflict', 'outside tts', 'number sequence'. Triggers (business): 'what does this error mean', 'explain this error to me', 'user gets error X', 'que signifie cette erreur', 'message d'erreur', 'what should the user do when they see this error'. Find known D365 F&O error patterns matching an error message or symptoms description. Matches against a built-in database of common errors (transaction conflicts, security issues, number sequences, posting errors, batch problems, etc.), resolves D365 label IDs from error text (e.g. user sees 'Number sequence not set up' -> finds @SYS70535 -> finds the throwing code), and searches the indexed codebase. Returns root causes, step-by-step resolution, label matches, and source code locations. [] When the error text contains a D365 label ID (e.g. '@SYS12345'), call search_labels first to resolve the label text, then call this tool with the resolved text. [] Also explains a Microsoft best-practice moniker: pass 'BPErrorFieldCurrencyDateTableEmpty' (or a symptom like 'strong name') to get the rule text from Microsoft's own catalogue, read out of the indexed D365 build. That is the rule TEXT -- run_best_practices_check_scoped is what decides whether an object breaks it. Set audienceType='business' for a plain-language explanation targeted at end users instead of developers.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
audienceTypeNoAudience type: 'developer' (default) for root-cause analysis, or 'business' for a plain-language explanation targeted at end users.developer
errorOrSymptomYesError message text or symptom description, e.g. 'update conflict', 'number sequence not set up', 'cannot create record outside tts'

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already mark the tool as read-only and idempotent, and the description is fully consistent with that. It adds meaningful behavioral detail beyond annotations: the tool matches against a built-in error database, resolves D365 label IDs, searches the indexed codebase, and returns root causes, step-by-step resolution, label matches, and source locations. It also clarifies the distinct role of the best-practice moniker path.

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 long and dense, but nearly every section earns its place: triggers, label-resolution routing, best-practice rule distinction, and audience behavior are all actionable. The structure front-loads WHEN guidance and then proceeds through resolution behavior and edge cases. It is slightly verbose due to the extensive trigger lists, but this density is functionally useful for triggering and disambiguation.

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?

Given two simple parameters, no output schema, and read-only annotations, the description covers what an agent needs: what input to supply, how to handle label IDs, how audienceType changes behavior, what the tool returns, and how it relates to sibling tools. Missing details such as exact output formatting are not necessary for correct invocation here.

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 already documents both parameters. The description adds value by giving concrete example values for errorOrSymptom ('update conflict', 'number sequence not set up') and by explaining audienceType='business' as plain-language explanation for end users rather than developer root-cause analysis. This enriches the schema without being redundant.

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 opens with a specific verb-resource pairing: 'Find known D365 F&O error patterns matching an error message or symptoms description.' It also names the matching mechanisms (built-in database, label resolution, codebase search) and explicitly differentiates from related tools like search_labels and run_best_practices_check_scoped. A developer trigger list further disambiguates when this tool, rather than a generic search 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?

The description gives explicit WHEN guidance for both developer and business audiences, including concrete trigger phrases. It also provides direct routing: if the error text contains a label ID, call search_labels first; for best-practice rule text, use this tool with the moniker, but use run_best_practices_check_scoped to decide whether an object violates it. This leaves little ambiguity about alternatives.

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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