gmail__mail_read
[gmail · risk:low] Read recent emails from the Gmail inbox
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| label | No | Optional label/folder to read from (defaults to INBOX) |
[gmail · risk:low] Read recent emails from the Gmail inbox
| Name | Required | Description | Default |
|---|---|---|---|
| label | No | Optional label/folder to read from (defaults to INBOX) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It only mentions 'risk:low' but does not specify whether emails are marked as read, pagination behavior, rate limits, or scope of 'recent'. This is minimal transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence, efficient for a simple tool. It could include more detail without losing conciseness, but it is not verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lacks essential context: it does not define 'recent', indicate number of emails returned, sorting order, or any behavior beyond the basic read action. For a tool with no output schema or annotations, this is insufficient for an agent to predict behavior accurately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema coverage is 100%, with the only parameter (label) already described. The description adds no additional meaning or syntax guidance beyond the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('read recent emails') and resource ('Gmail inbox'), distinguishing it from sibling tools like google_workspace__mail_search which likely searches emails rather than reading the inbox.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as google_workspace__mail_search or microsoft365__mail_search. No when-not or exclusion criteria are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool is prefixed with its service name, and within each service, tools have distinct actions (e.g., mail_read vs. availability_find). The duvera tools cover different subdomains like dev, finance, and food with no overlap, making selection unambiguous.
All tools follow the pattern service__action_object or service__category_action, using lowercase with underscores. The order of verb and noun varies slightly (e.g., package_track vs. boardingpass_show), but the naming is highly predictable and readable.
With 52 tools, the server is large but justified as a gateway aggregating many external services. Each tool corresponds to a common task for its service, so no tool feels extraneous, though the total number is high.
The tool surface covers a wide array of services but only provides one or two basic operations per service (mostly read-only). While this suits a quick-lookup gateway, deeper workflows (e.g., creating or updating resources) are missing, leaving gaps for many use cases.