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Glama

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'

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / properties / audienceType
      Added value: +{
+  "default": "developer",
+  "description": "Audience type: 'developer' (default) for root-cause analysis, or 'business' for a plain-language explanation targeted at end users.",
+  "type": "string"
+}
  2. First observed

TDQS

A4.8/5.0
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 adds substantial behavioral detail beyond that: it matches against a built-in error database, extracts D365 label IDs, searches the indexed codebase, returns root causes and resolutions, and distinguishes plain-language business explanations from developer-grade outputs. No contradiction with annotations exists.

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 but information-dense: the WHEN clause is front-loaded, trigger examples are compactly listed, and the [~] routing notes are separated. Every segment contributes operational guidance, so the length is justified by the tool's complexity.

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?

For a read-only diagnostic tool with no output schema, the description is remarkably complete. It states inputs, audience modes, known error categories, label-resolution behavior, best-practice moniker handling, and the return contents (root causes, step-by-step resolution, label matches, and source code locations). An agent has everything needed to invoke it correctly.

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 real semantic value by explaining audienceType's developer/business split and associating errorOrSymptom with concrete triggering examples ('update conflict', 'number sequence not set up'), which helps the agent formulate valid input.

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: 'Find known D365 F&O error patterns matching an error message or symptoms description.' It details what the tool does beyond simple search — resolving label IDs, searching the codebase, and returning root causes — and is clearly distinct from sibling tools like search_d365_code and search_labels.

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 explicitly opens with WHEN it should be used, provides concrete developer and business trigger phrases, and gives routing guidance: call search_labels first for '@SYS' label IDs, and use run_best_practices_check_scoped for deciding whether an object breaks a BP rule. This leaves no ambiguity about when to choose this tool over 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

A3.7/5.0
Disambiguation2/5

Multiple tools overlap heavily: ado_analyze_pr_impact and ado_review_xpp_pr both review PR impact; get_object_details/get_object_context, find_related_objects/get_relation_graph, and find_callers/find_references/find_change_impact duplicate each other. The consolidated dmf tool plus five dedicated dmf_* tools also create ambiguous entry points, making misselection likely.

Naming Consistency3/5

Most tools use readable snake_case verb_noun names, but conventions are mixed: domain prefixes (ado_, dmf_, odata_, appinsights_) coexist with generic verbs (find_, generate_, get_), and healthcheck breaks the pattern. Similar actions are also named inconsistently, such as find_callers vs find_change_impact and generate_data_entity vs find_entity_for_table.

Tool Count1/5

At 90 tools this is far beyond a focused ALM server; the surface spans ADO, DMF, OData, App Insights, code search, security, release notes, wiki, and code generation. Even for a broad D365 F&O scope, 90 tools is an extreme count that makes discovery and selection difficult.

Completeness4/5

The breadth leaves few obvious dead ends: ADO work items/PRs/wikis, DMF/OData data movement, code analysis, security, and release-note generation all have lifecycle coverage. Minor gaps exist, such as no PR creation/update/abandon and no work-item deletion, but agents can complete core workflows without significant workarounds.

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