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theYahia

@metarebalance/dadata-mcp

suggest_email

Autocomplete email addresses by suggesting domains and correcting typos as you type.

Instructions

Autocomplete email addresses. Suggests domains and corrects typos as user types.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
countNo
queryYesPartial email, e.g. 'john@gma'
Behavior2/5

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

With no annotations, the description must disclose behavioral traits. It mentions autocomplete, domain suggestions, and typo correction, but omits important details like whether results are limited, network dependency, or output format. This is insufficient for safe invocation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very concise (one sentence). However, it omits critical details such as the return value and parameter clarification, so it is not optimally informative for its length.

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

Completeness2/5

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

Given the lack of output schema and annotations, the description should explain what the tool returns (e.g., an array of email suggestions) and how parameters like 'count' affect behavior. It does not, leaving the agent with incomplete information.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers 50% of parameters with descriptions (query has an example, count lacks description). The tool description adds no parameter information beyond the schema, failing to compensate for the low coverage.

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?

The description clearly states it autocompletes email addresses, suggests domains, and corrects typos. This distinguishes it from sibling tools like suggest_address or clean_email, though it could be more specific about the suggestion mechanism.

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives like clean_email or other suggest_* tools. The description implies real-time typing assistance but does not explicitly state context or exclusions.

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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