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dogonthehorizon

Elrond MCP

by dogonthehorizon
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one checks system status, while the other analyzes proposals through hierarchical critique. There is no overlap in functionality or potential for confusion between monitoring system health and conducting proposal analysis.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern: check_system_status and consult_the_council. The naming is clear, descriptive, and follows the same convention throughout the toolset.

    Tool Count2/5

    With only two tools, this server feels severely under-equipped for what appears to be a thinking augmentation system. The tools cover basic status checking and proposal analysis, but there are likely many missing operations for a comprehensive augmentation system (e.g., configuration management, history tracking, different analysis modes).

    Completeness2/5

    The tool surface is significantly incomplete for a thinking augmentation system. While the two tools provide some functionality, there are obvious gaps: no way to configure the system, no historical analysis tracking, no ability to modify critique parameters, and no integration with external data sources. The tools feel like isolated endpoints rather than a complete system.

  • Average 3.8/5 across 2 of 2 tools scored.

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

    • No community issues 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?

    No annotations are provided, so the description carries the full burden. It discloses that the tool returns a dictionary with specific status information (API key availability, model configurations, health status), which adds useful context beyond a basic read operation. However, it doesn't cover other behavioral aspects like error handling, performance, or prerequisites, leaving gaps.

    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 appropriately sized and front-loaded, with the core purpose stated first and return details following. It uses two sentences efficiently, though the second sentence could be slightly more concise (e.g., by integrating the return details into the first sentence).

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

    Completeness3/5

    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 output schema, no annotations), the description is adequate but has clear gaps. It explains the return content well, but without annotations or output schema, it lacks details on format, error cases, or integration with the sibling tool, making it minimally viable.

    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, and schema description coverage is 100%, so no parameter information is needed. The description appropriately doesn't discuss parameters, earning a baseline score of 4 for not adding unnecessary details.

    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 checking the status of a specific system ('thinking augmentation system'), which is a specific verb+resource combination. However, it doesn't explicitly differentiate from the sibling tool 'consult_the_council', which might be related but isn't described here, preventing a perfect score.

    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. It doesn't mention the sibling tool 'consult_the_council' or any other context for usage decisions, leaving the agent with no explicit or implied usage instructions.

    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 the full burden of behavioral disclosure. It effectively describes the tool's behavior: running three specialized critique agents in parallel, analyzing the proposal from positive/neutral/negative perspectives, and synthesizing results. It mentions the parallel execution pattern and multi-perspective approach, which are valuable behavioral insights beyond basic function. However, it doesn't address potential limitations like processing time, token 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 well-structured and front-loaded with the core purpose in the first sentence. Each subsequent sentence adds specific value: explaining the three-agent architecture, describing the synthesis process, documenting the parameter, and outlining return values. There's no wasted text, and the information is presented in a logical flow from high-level purpose to implementation details.

    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 complexity (multi-agent analysis with synthesis), no annotations, and the presence of an output schema, the description provides good coverage. It explains the analysis methodology, parameter requirements, and return content. The output schema existence means the description doesn't need to detail return structure. However, for a complex tool with no annotations, it could benefit from mentioning potential constraints or limitations.

    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 schema has 0% description coverage, so the description must compensate. It provides meaningful context for the single parameter by specifying it should be 'Markdown-formatted' and should outline 'salient points of the solution to be analyzed.' This adds valuable semantic information beyond the bare schema type. However, it doesn't provide examples or more detailed formatting requirements.

    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 verbs ('analyze', 'runs', 'synthesizes') and resources ('proposal', 'three specialized critique agents'). It distinguishes itself from the only sibling tool (check_system_status) by focusing on proposal analysis rather than system monitoring. The description explains the hierarchical LLM critique and synthesis process, making the purpose unambiguous.

    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 by stating it analyzes 'a proposal' and outlines the specific process, but provides no explicit guidance on when to use this tool versus alternatives. Since there's only one sibling tool (check_system_status) with a completely different function, the lack of comparative guidance is less critical, but no explicit when/when-not instructions are 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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