ALM X++ MCP Server

summarize_for_stakeholder

Read-onlyIdempotent

WHEN: you have a large technical tool output (get_object_details, validate_best_practices, ado_analyze_workitem, detect_performance_issues...) and need it reframed for a non-technical audience. Calls the local Ollama instance (OLLAMA_HOST env var, default localhost:11434) to produce the summary. Model is configurable via ALMXPP_SUMMARIZE_MODEL (default: llama3.2).

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": {
      "text": "<text>"
    },
    "name": "summarize_for_stakeholder"
  }
}

Input Schema

Name	Required	Description	Default
`text`	Yes	The technical text to summarize/explain -- typically the raw output of another tool call.
`audience`	No	Target audience: 'executive', 'business-analyst', or 'developer'. Default 'business-analyst'.	business-analyst

Schema Changelog

Changes observed during successful MCP inspections.

AddedAug 12, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Adds valuable context beyond annotations: it calls a local Ollama instance, reads OLLAMA_HOST, and respects ALMXPP_SUMMARIZE_MODEL. This informs the agent of an external dependency and configuration that the readOnly/idempotent/destructive annotations do not convey.

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?

Three sentences, correctly front-loading the usage condition before implementation detail. Each sentence earns its place: when to use, what service is called, and how to configure the model.

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 simple two-parameter tool with annotations covering safety, the description supplies selection criteria, the external dependency, and configuration. It does not explicitly describe the return value format, but the name and 'produce the summary' make the output obvious; a no-output-schema tool might still benefit from one explicit sentence.

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?

Both parameters are fully described in the schema (100% coverage), so the baseline is 3. The description reinforces that 'text' is the raw output of another tool call, but that meaning is already present in the schema's description; no additional parameter detail is added.

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?

States a clear trigger ('large technical tool output') and purpose ('reframed for a non-technical audience'). The examples of source tools (get_object_details, validate_best_practices) tie it to a specific workflow and differentiate it from sibling tools, none of which summarize output for stakeholders.

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?

Explicitly opens with 'WHEN: you have a large technical tool output... and need it reframed for a non-technical audience', which is a precise selection condition. It doesn't discuss alternatives or when not to use it, but no sibling tool performs the same role.

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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