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ALM X++ MCP Server

by io.github.alimbenhelal-pro
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federated_search

Read-onlyIdempotent

Fan-out search across multiple ALM XPP MCP server instances in parallel and merge results using Reciprocal Rank Fusion (RRF). Useful when D365 code is split across multiple organisations or knowledge bases (e.g. one per client, one standard KB). Peer servers are configured via D365_FEDERATION_PEERS env var (comma-separated list of base URLs, e.g. https://org2.almxpp.com). The local server is always included as the primary source.

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": {
      "query": "<query>"
    },
    "name": "federated_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
topKNoNumber of results to return per server (default 5)
queryYesNatural language search query
peerApiKeysNoOptional MCP API keys for peer servers, comma-separated (same order as D365_FEDERATION_PEERS)

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 this as read-only and non-destructive. The description adds genuinely useful behavioral context: parallel fan-out, RRF merging, configured peers via environment variable, and the local server always being included as primary. This goes beyond what the annotations alone 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?

The description is front-loaded with the core operation and stays efficient. Each sentence earns its place: what it does, when to use it, how peers are configured, and the local server's role. The example in parentheses is helpful without bloating the text.

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 read-only search tool, this covers the essential operational details: purpose, configuration, merge behavior, and primary source. There is no output schema, but 'merge results' implies the return shape. It is complete enough for an agent to decide when to invoke it.

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 all three parameters are already documented: query, topK, and peerApiKeys. The description adds no additional parameter-level details, but no compensation is needed since the schema carries the full semantic load.

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 uses a specific verb ('fan-out search') and names the exact resource ('multiple ALM XPP MCP server instances') plus the merge technique (Reciprocal Rank Fusion). This clearly differentiates it from single-server search tools like search_d365_code by establishing the multi-server, federated scope.

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?

It explicitly states when to use it: 'when D365 code is split across multiple organisations or knowledge bases.' It does not name an alternative tool or say 'use X for single-server search,' but the condition is clear and the exclusion is easy to infer.

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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