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ai-zerolab

MCP Email Server

by ai-zerolab

get_emails_content

Retrieve the full content and body of emails using their IDs, with options for paginated reading of long messages.

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
mailboxNoThe mailbox to retrieve emails from.INBOX
email_idsYesList of email_id to retrieve (obtained from list_emails_metadata). Can be a single email_id or multiple email_ids.
body_offsetNoCharacter offset into each email body to start reading from. Use together with max_body_length to page through long emails: if a returned body ends with the '...[TRUNCATED]' marker, fetch the next chunk with body_offset += max_body_length.
account_nameYesThe name of the email account.
mark_as_readNoIf True, mark each successfully retrieved email as read. If marking fails, a warning is logged and retrieval still succeeds.
max_body_lengthNoMaximum number of body characters to return, counted from body_offset. If the body extends past this window, the '...[TRUNCATED]' marker is appended after the requested body window.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailsYes
failed_idsYes
requested_countYes
retrieved_countYes
Behavior4/5

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

Despite no annotations, the description and parameter details disclose key behaviors: pagination via body_offset, truncation marker, and conditional marking as read with fallback logging. It lacks explicit statement about being non-destructive, but overall good transparency.

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, front-loaded with the core action and prerequisite. No wasted words, efficient and clear.

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?

Given the 6 parameters and existence of an output schema, the description provides the necessary context: what the tool does and when to use it. The schema fills in parameter details. Could mention output format briefly, but overall adequate.

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 coverage is 100%, so baseline is 3. The tool description itself does not add parameter meaning, but the schema's parameter descriptions are detailed (e.g., body_offset pagination, mark_as_read behavior). The description's role is minimal here.

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 tool retrieves full content (including body) of emails by email_id, and explicitly mentions the prerequisite step to use list_emails_metadata, distinguishing it from sibling tools.

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?

The description provides a clear usage context: use after list_emails_metadata to get email_ids. It does not mention when not to use or alternatives, but the guidance is sufficient.

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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