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Email Sending MCP

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List Suppressions

list-suppressions
Read-only

Retrieve suppressed email addresses, optionally filtered by origin (bounce, complaint, or manual), to see who is blocked from receiving emails.

Instructions

Purpose: List email addresses on the suppression list. Suppressed addresses never receive emails from the account. Optionally filter by origin: "bounce" (added automatically after a hard bounce), "complaint" (added automatically after a spam complaint), or "manual" (added via the API or dashboard).

NOT for: Checking a single address (use get-suppression). Not for listing contacts (use list-contacts).

Returns: For each suppression: email, id, origin, source_id (when present), created_at. Use pagination (limit, after/before) for large lists.

When to use: User says "show my suppression list", "who is suppressed?", or "why isn't this person receiving emails?" combined with a broad look at suppressed addresses.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
afterNoSuppression ID after which to retrieve more (for forward pagination). Cannot be used with "before".
limitNoNumber of suppressions to retrieve. Default: 20, Max: 100, Min: 1
beforeNoSuppression ID before which to retrieve more (for backward pagination). Cannot be used with "after".
originNoOnly return suppressions with this origin: "bounce", "complaint", or "manual".
Behavior5/5

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

Annotations provide readOnlyHint=true, but the description adds rich behavioral context: explains that suppressed addresses never receive emails, describes automatic origin sources (bounce, complaint), and discloses return fields. No contradiction with annotations.

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?

Though longer than minimal, every section serves a purpose: Purpose, NOT for, Returns, When to use. Proper use of bold headings and concise bullet-like phrases makes it scannable and free of fluff.

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

Completeness5/5

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

For a list tool with 4 optional parameters and no output schema, the description is complete: it covers what it returns, pagination, origin filtering, and disambiguation from related tools. No significant missing context.

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 covers 100% of parameters with descriptions, so baseline is 3. The description enhances this by explaining how origin values map to real-world events and elaborating on pagination usage (after/before), adding meaning beyond the schema.

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?

Description states a specific verb and resource ('List email addresses on the suppression list') and clearly distinguishes itself from siblings by explicitly naming get-suppression and list-contacts as alternatives. This goes beyond a bare statement of purpose.

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

Usage Guidelines5/5

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

The 'NOT for' section explicitly lists what the tool is not for and names the correct alternative tools. The 'When to use' section provides concrete example user requests, making usage context unmistakable.

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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