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text_extract_emails

Extract email addresses from text using multiple regex modes. Filter by domain, remove duplicates, validate syntax, and analyze TLD spread.

Instructions

Menu ID: extract_emails. Extract Emails. Extract email addresses from text with strict, standard, and permissive regex modes. Filter by domain, deduplicate, validate syntax, and analyse TLD spread. Use describe_tool with tool_id "extract_emails" for full page guidance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes
extractionModeYes
removeDuplicatesYes
sortResultsYes
includeContextYes
contextLengthYes
extractDomainsYes
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It mentions extraction and analysis capabilities but omits critical details such as whether the tool modifies input, expected output format, error handling, or side effects. Given the absence of an output schema, this is a significant gap.

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

Conciseness4/5

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

The description is two sentences, efficiently summarizing key features and directing to more detail. It is well-structured with a clear verb-resource-action pattern, though the list of operations could be more organized.

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 7 required parameters, no output schema, and complex regex-based functionality, the description is insufficient for an agent to invoke the tool correctly without further guidance. It relies on 'describe_tool' but does not cover parameter constraints or return format.

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?

With 0% schema coverage, the description must compensate. It explains 'extractionMode' (strict, standard, permissive), 'removeDuplicates', and 'extractDomains', adding meaning beyond parameter names. However, it does not clarify 'sortResults', 'includeContext', or 'contextLength', leaving these ambiguous.

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 action ('Extract email addresses'), the resource ('text'), and specific operations (with strict/standard/permissive modes, filtering by domain, deduplication, validation, TLD analysis). It distinguishes from siblings like text_extract_urls by focusing on emails and listing specialized features.

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 explains available modes and operations, and directs users to 'use describe_tool' for full guidance. While it does not explicitly contrast with alternatives or specify when not to use, it provides sufficient context for typical use cases.

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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