Gmail MCP Server

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extract_addresses

Collect unique email addresses from Gmail messages that match a search query. Scans fields like from, to, cc, and reply-to to build contact lists.

Instructions

Extract unique email addresses from messages matching a Gmail query. Useful for building contact lists.

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": "extract_addresses"
  }
}

Input Schema

Name	Required	Description
`query`	Yes	Gmail search query to find messages
`maxMessages`	No	Maximum messages to scan
`fields`	No	Which header fields to extract addresses from

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.0.0Jun 21, 2026

TDQS

B3.4/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden. It implies a non-destructive read operation by saying 'extract', but does not explicitly disclose behavioral traits such as required permissions, side effects, or rate limits. The mention of 'unique' is helpful but insufficient.

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?

Two sentences, no waste. The key action is front-loaded. Every part earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool has 3 parameters and no output schema. Description does not mention what is returned (e.g., array of strings), performance considerations, or whether results are deduplicated. For a tool without output schema, more context is needed.

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?

Input schema covers all parameters with descriptions (100% coverage). The description adds no new parameter semantics beyond reinforcing the query parameter's role. Baseline 3 is appropriate since schema does the heavy lifting.

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 verb 'extract', the resource 'unique email addresses', and the scope 'from messages matching a Gmail query'. It distinguishes from siblings like search_emails (which returns messages) and get_frequent_contacts (which likely returns contacts, not extracted from query). The specific phrase 'building contact lists' adds context.

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?

The description provides a use case hint ('useful for building contact lists') but does not explicitly state when to use this tool versus alternatives like get_frequent_contacts or search_emails. No when-not-to-use guidance or alternative tool names are mentioned.

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