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avivshafir

revenuebase-mcp-server

batch_process_email_status

Check the status of batch email processing jobs in RevenueBase to monitor progress and completion.

Instructions

Retrieves status of batch email processing job.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
process_idYes

Implementation Reference

  • server.py:11-26 (handler)
    The main handler function that executes the tool logic: checks for API key, constructs headers and payload, makes a POST request to the Revenuebase API endpoint for batch email status, and returns the JSON response.
    def batch_process_email_status(process_id: int) -> dict:
        """
        Retrieves status of batch email processing job.
        """
        if not api_key:
            raise RuntimeError("Environment variable REVENUEBASE_API_KEY is not set")
        url = "https://api.revenuebase.ai/v1/batch-process-email-status"
        headers = {
            "x-key": api_key,
            "Content-Type": "application/json",
            "Accept": "application/json",
        }
        payload = {"process_id": process_id}
        resp = requests.post(url, json=payload, headers=headers, verify=False)
        resp.raise_for_status()
        return resp.json()
  • server.py:10-10 (registration)
    Registers the batch_process_email_status function as an MCP tool using the FastMCP decorator.
    @mcp.tool()
  • Implicit schema via type hints: input process_id as int, output as dict.
    def batch_process_email_status(process_id: int) -> dict:
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states this is a retrieval operation, implying read-only behavior, but doesn't cover aspects like error handling, rate limits, authentication needs, or what the status output includes (e.g., progress, errors). For a tool with no annotations, this leaves significant gaps.

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 with zero waste. It's front-loaded with the core action and resource, making it easy to scan and understand quickly without unnecessary details.

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 no annotations, no output schema, and low schema coverage, the description is incomplete. It lacks details on behavioral traits, parameter usage, and expected return values (e.g., status format, possible states). For a tool that retrieves job status, more context is needed to guide effective use.

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?

Schema description coverage is 0%, so the description must compensate. It implies the 'process_id' parameter is needed to identify the job, adding some meaning beyond the schema's title ('Process Id'). However, it doesn't explain the parameter's format, source (e.g., from 'batch_email_submission'), or constraints, providing only basic context.

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 verb ('Retrieves') and resource ('status of batch email processing job'), making the purpose understandable. However, it doesn't distinguish this tool from potential sibling tools like 'queued_process' or 'cancel_process' that might also relate to batch processing, missing full differentiation.

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. The description doesn't mention prerequisites (e.g., needing a process_id from a prior job), exclusions, or comparisons to siblings like 'queued_process' or 'batch_email_submission', leaving usage context unclear.

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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