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count-daily-emails

Count emails received daily within a specified date range to track email volume patterns and manage inbox activity.

Instructions

Count emails received for each day in a date range

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
start_dateYesStart date in YYYY-MM-DD format
end_dateYesEnd date in YYYY-MM-DD format

Implementation Reference

  • Registration of the 'count-daily-emails' tool, including its name, description, and JSON schema for input validation (start_date and end_date required).
    types.Tool(
        name="count-daily-emails",
        description="Count emails received for each day in a date range",
        inputSchema={
            "type": "object",
            "properties": {
                "start_date": {
                    "type": "string",
                    "description": "Start date in YYYY-MM-DD format",
                },
                "end_date": {
                    "type": "string",
                    "description": "End date in YYYY-MM-DD format",
                },
            },
            "required": ["start_date", "end_date"],
        },
    ),
  • Main handler for the 'count-daily-emails' tool. Parses start and end dates, loops through each day, performs IMAP search for emails received 'ON' that date using the count_emails_async helper, handles timeouts, and formats results as a markdown table of daily counts.
    elif name == "count-daily-emails":
        start_date = datetime.strptime(arguments["start_date"], "%Y-%m-%d")
        end_date = datetime.strptime(arguments["end_date"], "%Y-%m-%d")
        
        result_text = "Daily email counts:\n\n"
        result_text += "Date | Count\n"
        result_text += "-" * 30 + "\n"
        
        current_date = start_date
        while current_date <= end_date:
            date_str = current_date.strftime("%d-%b-%Y")
            search_criteria = f'(ON "{date_str}")'
            
            try:
                async with asyncio.timeout(SEARCH_TIMEOUT):
                    count = await count_emails_async(mail, search_criteria)
                    result_text += f"{current_date.strftime('%Y-%m-%d')} | {count}\n"
            except asyncio.TimeoutError:
                result_text += f"{current_date.strftime('%Y-%m-%d')} | Timeout\n"
            
            current_date += timedelta(days=1)
        
        return [types.TextContent(
            type="text",
            text=result_text
        )]
  • Helper function used by the count-daily-emails handler to asynchronously count the number of emails matching an IMAP search criteria (e.g., emails received on a specific date). Runs IMAP search in executor to avoid blocking.
    async def count_emails_async(mail: imaplib.IMAP4_SSL, search_criteria: str) -> int:
        """Asynchronously count emails matching the search criteria."""
        loop = asyncio.get_event_loop()
        try:
            _, messages = await loop.run_in_executor(None, lambda: mail.search(None, search_criteria))
            return len(messages[0].split()) if messages[0] else 0
        except Exception as e:
            raise Exception(f"Error counting emails: {str(e)}")
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does (count emails per day) but doesn't describe important behavioral aspects: whether it requires authentication, how it handles large date ranges, what format the results are returned in, or any rate limits. For a tool with zero annotation coverage, this leaves significant gaps in understanding its operational characteristics.

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?

The description is a single, efficient sentence that communicates the core functionality without unnecessary words. It's appropriately sized for a simple counting tool and front-loads the essential information. Every word earns its place.

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 annotations and output schema, the description should provide more complete context for this tool. While it states what the tool does, it doesn't explain what the output looks like (e.g., returns a list of day-count pairs), doesn't mention authentication requirements, and doesn't provide error handling guidance. For a tool with no structured behavioral metadata, the description is insufficiently complete.

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?

The schema description coverage is 100%, with both parameters clearly documented in the input schema. The description mentions 'date range' which aligns with the two date parameters, but adds no additional semantic context beyond what's already in the schema (like date format requirements or inclusive/exclusive range behavior). This meets the baseline expectation when schema coverage is complete.

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 the tool's purpose as 'Count emails received for each day in a date range', which specifies the verb (count), resource (emails), and scope (per day in date range). It distinguishes from sibling tools like 'get-email-content' (retrieve content) and 'send-email' (send operation), but doesn't explicitly differentiate from 'search-emails' which might also involve date filtering.

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?

The description provides no guidance on when to use this tool versus alternatives like 'search-emails' or 'get-email-content'. It doesn't mention prerequisites, exclusions, or specific contexts where this counting operation is preferred over other email-related tools.

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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