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kukapay

ethereum-validator-queue-mcp

by kukapay
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap: get_activation_queue focuses on activation statistics, get_exit_queue on exit statistics, and get_validator_status on individual validator details. The descriptions clearly differentiate between queue-level and validator-specific operations, eliminating any potential confusion.

    Naming Consistency5/5

    All three tools follow a consistent verb_noun pattern with 'get_' prefix and descriptive suffixes (_activation_queue, _exit_queue, _validator_status). The naming is uniform, predictable, and clearly communicates each tool's function without any stylistic deviations.

    Tool Count4/5

    Three tools is appropriate for the narrow scope of Ethereum validator queue monitoring, covering activation, exit, and individual status queries. While slightly minimal, each tool earns its place by addressing distinct aspects of the domain without redundancy or obvious omissions.

    Completeness4/5

    The toolset provides comprehensive read-only coverage for validator queue monitoring, including both aggregate queue statistics and individual validator status. A minor gap exists in the lack of write operations (e.g., initiating validator actions), but this aligns with the server's apparent monitoring focus, and agents can work effectively with the provided tools.

  • Average 3.9/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • 0 of 1 community issues answered or closed in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior3/5

    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 describes the return format in detail but doesn't mention error conditions, rate limits, authentication requirements, or whether this is a read-only operation. The description adds value by specifying the return structure but misses important behavioral context.

    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 well-structured with clear sections for Args and Returns, and every sentence adds value. It could be slightly more concise by combining some return value descriptions, but overall it's efficiently organized.

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

    Completeness4/5

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

    Given the tool has an output schema (though not shown), the description provides comprehensive return value details. For a single-parameter tool with no annotations, the description covers the core functionality well but could benefit from more behavioral context like error handling.

    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 description provides crucial semantic information about the pubkey parameter that isn't in the schema (0% coverage), specifying it must be a '48-byte hex string starting with '0x''. This adds significant value beyond the basic string type in the schema.

    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 specific action ('Get status') and target resource ('specific Ethereum validator by public key'), distinguishing it from sibling tools like get_activation_queue and get_exit_queue which handle different validator lifecycle aspects.

    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 or in what context. The description only states what it does, not when it's appropriate compared to sibling tools.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior3/5

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

    No annotations are provided, so the description carries the full burden. It discloses that the tool returns statistics (a read operation) and includes estimated wait time based on a daily exit rate, which adds useful context. However, it lacks details on permissions, rate limits, 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 front-loaded with the purpose, followed by a bulleted list of return values. Every sentence earns its place, with no wasted words, making it efficient and well-structured.

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

    Completeness4/5

    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 with an output schema), the description is complete enough. It explains the purpose and details the return values, though it could benefit from usage guidance relative to sibling tools to fully cover context.

    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?

    There are 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description appropriately focuses on the return values without redundant parameter information, meeting the baseline for tools with no parameters.

    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 specific action ('Get current Ethereum validator exit queue statistics') and distinguishes it from sibling tools like 'get_activation_queue' and 'get_validator_status' by focusing on exit queue data rather than activation 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 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 'get_activation_queue' or 'get_validator_status'. The description only states what it returns, not the context or scenarios where it should be preferred over sibling tools.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • 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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