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Search emails

search_emails
Read-onlyIdempotent

Search real product emails (welcome, receipts, onboarding, re-engagement…) by description or brand. Each result is one email with its desktop and/or mobile image; when hasHtml is true, get_email_html returns the original HTML to adapt. Needs Pro.

Input Schema

NameRequiredDescriptionDefault
appIdNoLimit to these brands
limitNo
queryNoFree text, e.g. 'welcome email with a big hero image'
offsetNoSkip this many results (pagination)
flowIntentsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, non-destructive, closed-world, so the safety profile is covered. The description adds real value by disclosing the result granularity (one email per result with desktop and/or mobile image) and the hasHtml flag semantics, but says nothing about pagination behavior or result caps.

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?

Three tight sentences with no filler; the core search behavior comes first, then the result shape, then the follow-up routing and prerequisite. Every clause earns its place.

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 no output schema, the description usefully characterizes the return unit and the hasHtml signal, and names the follow-up tool. The remaining gap is that flow-intent filtering and pagination are never mentioned for a 5-parameter 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?

At 60% schema coverage the description maps 'by description' to query and 'by brand' to appId, which is useful. However flowIntents (a 15-value enum with no schema description) and limit are left unexplained, so the description does not compensate for the coverage gap.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource (search real product emails) plus the two search axes (description or brand) and what one result is. It does not contrast itself with the many sibling search_* tools (search_flows, search_screens), so an agent must infer the boundary, but the purpose itself is unambiguous.

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?

Gives the prerequisite ('Needs Pro') and routes the agent forward: when hasHtml is true, call get_email_html to get the original HTML. It lacks an explicit when-not/alternative statement versus other search tools, which keeps it off a 5.

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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