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ALM X++ MCP Server

by io.github.alimbenhelal-pro
OverviewDiscussionsAdmin

generate_release_note_document

Idempotent

WHEN: you have already called prepare_release_note_context and analyzed its 'objects' array yourself, producing a findings JSON array per the 'instructions' field it returned. This tool renders that findings array into a downloadable Word (.docx, detailed appendix) and PowerPoint (.pptx, executive summary) release note and returns their download URLs. Does NOT call any LLM itself -- the reasoning must already be done by you.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "v1": "<v1>",
      "v2": "<v2>",
      "customModelLabel": "<customModelLabel>",
      "findingsJson": "<findingsJson>",
      "touchedAdded": "<touchedAdded>",
      "touchedRemoved": "<touchedRemoved>",
      "touchedModified": "<touchedModified>"
    },
    "name": "generate_release_note_document"
  }
}

Input Schema

NameRequiredDescriptionDefault
v1YesOlder D365FO version (same value passed to prepare_release_note_context).
v2YesNewer D365FO version (same value passed to prepare_release_note_context).
findingsJsonYesJSON array of your findings, one per object from prepare_release_note_context's 'objects' array. Schema: [{"aotType":"...","objectName":"...","changeType":"Added|Removed|Modified","riskLevel":"Critical|Warning|Info|None","whatChanged":"...","documentedInMsLearn":true|false,"msLearnReference":"...","undocumentedReason":"...","regressionRisk":"...","opportunity":"...","recommendation":"..."}]
touchedAddedYestouchedAdded count returned by prepare_release_note_context.
touchedRemovedYestouchedRemoved count returned by prepare_release_note_context.
businessContextNoOptional business/functional context (same value passed to prepare_release_note_context), included in the Word document.
touchedModifiedYestouchedModified count returned by prepare_release_note_context.
customModelLabelYesCustom model label(s) -- use the customModelLabel field returned by prepare_release_note_context.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already mark the tool as idempotent and non-destructive, and the description adds meaningful behavioral context: it renders files, returns download URLs, does not invoke an LLM, and depends on prior manual analysis. This goes beyond the structured hints, though it does not describe failure modes, auth requirements, or file lifecycle details.

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 description is two compact sentences with no filler. The WHEN condition is front-loaded, the action is stated directly, and the critical non-LLM caveat is placed at the end where it reinforces rather than distracts.

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 an 8-parameter tool with 100% schema coverage and no output schema, the description provides the essential workflow context: the prerequisite call, the expected input structure origin, the two output formats, and the return type. It could go slightly further by describing what the agent should do with the returned URLs or how errors manifest, but nothing critical is missing.

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 fully documents every parameter. The description adds a small amount of extra meaning by tying findingsJson to the instructions field from prepare_release_note_context and clarifying the relationship between v1/v2/customModelLabel and the same values passed to the context tool, but this is marginal beyond 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 clearly states the tool's specific action: rendering a pre-analyzed findings array into downloadable Word (.docx) and PowerPoint (.pptx) release notes and returning their URLs. It distinguishes itself from the sibling prepare_release_note_context by explicitly framing this as the post-analysis rendering step and by noting it does not call any LLM itself.

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 an explicit precondition: call this only after prepare_release_note_context has been used and the agent has manually analyzed the objects array to produce findings. It also warns that the tool does no reasoning, so the agent must not expect it to fill that gap. However, it does not name alternative tools or formally state when not to use it beyond the implied precondition.

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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