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ALM X++ MCP Server

by io.github.alimbenhelal-pro

dmf_transform_excel

Transform a multi-sheet Excel (.xlsx) into DMF-ready rows using a data-driven JSON mapping -- equivalent to FO_TransformExcelForDMF. NO FO credentials required (offline transform). Provide the workbook via ONE of: sourceUrl (Blob/SharePoint SAS URL or Graph downloadUrl), filePath (local .xlsx) or fileContentBase64 (inline upload, small files only). The mappingJson spec supports: sourceSheet, targetSheet, columnMappings {source->target}, conditionalValues [{sourceColumn,matches[],values{}}], staticValues{}, deduplicateOn[] and autoGeneratedFields (array OR object keyed by sheet, e.g. {"Products V2":["PRODUCTNUMBER"]}) which are removed so FO generates them. Returns CSV (default, feed to dmf_import_file) or JSON (feed to odata_upsert_rows). Set listSheetsOnly=true to just inspect the workbook's sheet names.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNoOutput format: 'csv' (default) or 'json'.csv
filePathNoLocal path to a .xlsx file. Provide one of sourceUrl/filePath/fileContentBase64.
sheetNameNoOverride the source sheet name. Empty = use mappingJson.sourceSheet, else the first sheet.
sourceUrlNoURL to the .xlsx (Blob/SharePoint SAS or Graph downloadUrl). Provide one of sourceUrl/filePath/fileContentBase64.
outputPathNoOptional file path to also write the full transformed result to.
mappingJsonNoMapping spec JSON (columnMappings, conditionalValues, staticValues, deduplicateOn, autoGeneratedFields, sourceSheet). Optional when listSheetsOnly=true.
listSheetsOnlyNoIf true, only list the workbook's sheet names (diagnostic). mappingJson not required.
fileContentBase64NoBase64-encoded .xlsx content (inline upload, small files). Provide one of sourceUrl/filePath/fileContentBase64.

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the disclosure burden and does so well: it notes no FO credentials are needed, base64 upload is only for small files, autoGeneratedFields are removed so FO regenerates them, and output is CSV or JSON with downstream consumers. It stops short of detailing side effects such as outputPath file writes or error behavior, but the core behavior is transparent.

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 text is dense but organized: main action, credential caveat, source alternatives, mapping spec summary, outputs, and diagnostic mode. Every sentence carries distinct information, and there is no filler or tautology.

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 complex 8-parameter tool with no annotations and no output schema, the description covers all essential usage aspects: input source alternatives, mapping JSON structure, output formats and downstream routing, plus a diagnostic mode. An agent has enough to select and invoke it correctly.

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

Parameters5/5

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

Schema description coverage is 100%, but the description adds substantial meaning beyond it: the mappingJson supported keys and shapes, the sourceUrl variants, file size caveat, format-to-downstream-tool mapping, and listSheetsOnly diagnostic purpose are all explained. This deeply complements the parameter 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 opens with a specific verb-resource pair: 'Transform a multi-sheet Excel (.xlsx) into DMF-ready rows using a data-driven JSON mapping.' It also states it is the offline equivalent of FO_TransformExcelForDMF and explicitly routes output to dmf_import_file or odata_upsert_rows, which distinguishes it from sibling 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?

Clear context is given: use it to prepare Excel data offline before importing via dmf_import_file or upserting via odata_upsert_rows, and use listSheetsOnly for inspection. However, it never explicitly states when not to use it (e.g., when data is already DMF-ready) or compares it to a direct alternative.

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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