MCP Aruba Email & Calendar Server

by eteocuenca

Overview Schema Source Related Servers Discussions Admin

Python

Local

search_emails

Find emails by subject or body content, filter by folder or date, and limit results to locate the messages you need.

Instructions

Search emails by subject or body content.

Args: query: Search query string (searches in subject and body) folder: Mail folder to search in (default: INBOX) from_date: Only emails from this date onwards (format: DD-MMM-YYYY, e.g., "01-Dec-2024") limit: Maximum number of results (default: 10, max: 50)

Returns: List of matching emails

Example: search_emails(query="API", from_date="01-Dec-2024", limit=5)

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": "search_emails"
  }
}

Input Schema

Name	Required	Default
`limit`	No
`query`	Yes
`folder`	No	INBOX
`from_date`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.1Nov 9, 2026

TDQS

A3.6/5.0

Behavior3/5

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

No annotations exist, so the description carries the full behavioral burden. It discloses the default folder and the 50-result cap, but says nothing about pagination, whether results are truncated silently, or how matching (substring vs. token) works.

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 Args/Returns/Example structure is front-loaded and easy to scan, with a concrete call example at the end. A few lines restate schema defaults and could be trimmed without loss.

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 four-parameter read tool with an output schema already defining return shape, the description covers the searchable fields, date format, folder scope, and result cap. The remaining gap is pagination/truncation behavior, which is minor at this complexity.

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 description coverage is 0%, so the description must compensate, and it largely does: it documents all four params and adds the DD-MMM-YYYY date format with a worked example, plus the max value of 50 that the schema omits. The default values it repeats are redundant with the schema defaults.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a concrete verb and resource ('Search emails by subject or body content') so the agent can tell it apart from list_emails or read_email by what it operates on. It stops short of explicitly contrasting itself with those siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the name and the query parameter, but there is no statement of when to prefer this over list_emails (browse a folder) or read_email (fetch one message). No exclusions or prerequisites are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.