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ai-zerolab

MCP Email Server

by ai-zerolab

get_emails_content

Fetch the complete content and body of specific emails using their email IDs, obtained from listing metadata. Optionally mark them as read.

Instructions

Get the full content (including body) of one or more emails by their email_id. Use list_emails_metadata first to get the email_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
account_nameYesThe name of the email account.
email_idsYesList of email_id to retrieve (obtained from list_emails_metadata). Can be a single email_id or multiple email_ids.
mailboxNoThe mailbox to retrieve emails from.INBOX
mark_as_readNoIf True, mark each successfully retrieved email as read. If marking fails, a warning is logged and retrieval still succeeds.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailsYes
requested_countYes
retrieved_countYes
failed_idsYes
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 does not disclose that retrieving emails may update read status (via the mark_as_read parameter) or mention authentication, rate limits, or error handling. The parameter descriptions hint at behavior, but the main description lacks these details.

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 wasted words. The first sentence states the core function, the second gives a critical usage hint. Highly efficient.

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

Completeness3/5

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

Given 4 parameters (2 required) and an output schema, the description covers the essential purpose and prerequisite. However, it omits context about optional parameters (mailbox, mark_as_read) beyond what's in the schema, which may affect usage decisions.

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 coverage is 100%, but the description adds value by noting that email_ids are obtained from list_emails_metadata and that content includes the body. This goes beyond the schema's parameter descriptions.

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 explicitly states the verb 'get', the resource 'full content of emails', and the identifier 'email_id'. It distinguishes from sibling tool 'list_emails_metadata' by mentioning its role in obtaining the ID.

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?

Provides explicit guidance: 'Use list_emails_metadata first to get the email_id.' This indicates a prerequisite and implies that direct use without the ID is not appropriate.

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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