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kukapay

ethereum-validator-queue-mcp

get_activation_queue

Retrieve real-time Ethereum validator activation queue statistics including queue length, active validators, total balance entering, and estimated wait time to monitor staking dynamics.

Instructions

Get current Ethereum validator activation queue statistics.

Returns:
    A string containing:
    - Current activation queue length (number of validators waiting to activate)
    - Total active validators
    - Total balance of entering validators in ETH
    - Estimated wait time (based on ~900 activations per day)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • main.py:11-37 (handler)
    The handler function that fetches Ethereum validator activation queue data from the beaconcha.in API, processes it, and returns formatted statistics including queue length, active validators, entering balance, and estimated wait time.
    async def get_activation_queue() -> str:
        """Get current Ethereum validator activation queue statistics.
        
        Returns:
            A string containing:
            - Current activation queue length (number of validators waiting to activate)
            - Total active validators
            - Total balance of entering validators in ETH
            - Estimated wait time (based on ~900 activations per day)
        """
        async with httpx.AsyncClient() as client:
            try:
                response = await client.get("https://beaconcha.in/api/v1/validators/queue")
                response.raise_for_status()
                data = response.json()["data"]
                entering = data.get("beaconchain_entering", 0)
                validators_count = data.get("validatorscount", 0)
                entering_balance = data.get("beaconchain_entering_balance", 0) / 1e9 if data.get("beaconchain_entering_balance") else 0
                return (
                    f"Current activation queue length: {entering} validators\n"
                    f"Total active validators: {validators_count}\n"
                    f"Entering validators balance: {entering_balance:.2f} ETH\n"
                    f"Estimated wait time: Approximately {entering / 900:.1f} days "
                    f"(assuming ~900 activations per day)"
                )
            except Exception as e:
                return f"Error fetching activation queue data: {str(e)}"
  • main.py:10-10 (registration)
    The @mcp.tool() decorator registers the get_activation_queue function as an MCP tool.
    @mcp.tool()
  • main.py:12-20 (schema)
    Docstring defining the tool's purpose and output format, serving as the schema for the tool.
    """Get current Ethereum validator activation queue statistics.
    
    Returns:
        A string containing:
        - Current activation queue length (number of validators waiting to activate)
        - Total active validators
        - Total balance of entering validators in ETH
        - Estimated wait time (based on ~900 activations per day)
    """
Behavior4/5

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

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it's a read operation (implied by 'Get'), returns specific statistical data, includes an estimated wait time calculation methodology ('based on ~900 activations per day'), and describes the return format. However, it doesn't mention potential rate limits, authentication requirements, or error conditions.

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 perfectly structured and concise: a clear purpose statement followed by a bulleted list of exactly what data is returned. Every sentence earns its place with zero wasted words, and the information is front-loaded appropriately.

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

Completeness5/5

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

Given the tool's simplicity (0 parameters, no annotations, but has output schema), the description is complete enough. It clearly explains what the tool does and what data it returns, which is sufficient for a straightforward read-only statistical query tool. The output schema will provide additional structured details about the return format.

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

Parameters4/5

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

The tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, focusing instead on the return value semantics which is the right approach for a parameterless tool.

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 tool's purpose with specific verb ('Get') and resource ('Ethereum validator activation queue statistics'), distinguishing it from sibling tools like 'get_exit_queue' and 'get_validator_status' by focusing specifically on activation queue data rather than exit queue or individual validator status.

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

Usage Guidelines3/5

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

The description implies usage context through the specific data it returns (activation queue statistics), but doesn't explicitly state when to use this tool versus alternatives like 'get_exit_queue' or 'get_validator_status'. No explicit guidance on when-not-to-use or prerequisites is provided.

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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