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Email MCP Server

Email MCP Server

by 123jixinyu
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

get_email_stats

Read-only

Analyze mailbox email statistics: volume, top senders, daily trends, and read/flagged counts.

Instructions

Get email statistics and analytics for a mailbox. Shows volume, top senders, daily trends, and read/flagged counts.

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": {
      "account": "<account>"
    },
    "name": "get_email_stats"
  }
}

Input Schema

NameRequiredDescriptionDefault
periodNoTime period: day, week, or monthweek
accountYesAccount name
mailboxNoMailbox path (default: INBOX)INBOX

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.3

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds value by specifying the type of analytics returned (volume, top senders, daily trends), which is especially useful given the absence of an output schema. No contradictions.

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 short sentences (14 words) that directly state the purpose and key output categories. It is front-loaded with the main verb and resource, with zero filler.

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?

With all parameters documented and safety annotations present, the description adequately covers the tool's purpose and return contents. It could benefit from explicit use-case distinction, but overall it is sufficient for an agent to select and invoke the tool.

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?

The input schema provides descriptions for all three parameters (account, period, mailbox), covering 100% of semantics. The tool description adds no additional parameter information, so the baseline for high schema coverage applies.

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 identifies the tool as retrieving email statistics/analytics, naming specific metrics (volume, top senders, daily trends, read/flagged counts). This distinguishes it from siblings like list_emails or get_email, which fetch individual messages or lists.

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?

No explicit guidance on when to use this tool versus alternatives is present. The description implies usage for aggregate analytics but does not mention when to avoid it or name alternative tools, so only implied context exists.

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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