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kukapay

liquidity-pools-mcp

by kukapay

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap between tools. The tool has a single, clearly defined purpose of fetching liquidity pools from DexScreener API.

    Naming Consistency5/5

    The single tool follows a clear verb_noun pattern (get_liquidity_pools), which is consistent within this minimal set. There are no other tools to create inconsistency.

    Tool Count2/5

    A single tool is too few for a server focused on liquidity pools, which typically involves operations like creating, updating, or analyzing pools beyond just fetching. This feels incomplete for the domain.

    Completeness2/5

    The tool surface is severely incomplete for liquidity pool management. It only provides a fetch operation (get), missing essential CRUD operations like create, update, or delete pools, as well as analytical tools for deeper insights.

  • Average 4.3/5 across 1 of 1 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

  • 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: it fetches data from an external API (DexScreener), specifies the return format (a markdown table with detailed metrics), and mentions logging/request handling via the MCP context parameter. However, it lacks details on error handling, rate limits, or authentication requirements.

    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, followed by organized sections for Args and Returns. Every sentence adds value: the first sentence states the action, the Args section details parameters with examples, and the Returns section specifies the output format. There is no wasted text.

    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 moderate complexity (2 parameters, no annotations, no output schema), the description is largely complete. It covers the purpose, parameters, and return format adequately. However, it lacks information on potential errors, rate limits, or authentication, which would be helpful for a tool interacting with an external API.

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

    Parameters5/5

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

    The description adds significant meaning beyond the input schema, which has 0% description coverage. It provides concrete examples for both parameters (e.g., 'bsc' for chain_id, a sample token address), clarifies data types (str), and explains the purpose of each parameter in the context of the API call. This fully compensates for the schema's lack of documentation.

    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 ('Fetch all liquidity pools') with the target resource ('for a given chain ID and token address from DexScreener API'). It provides a complete verb+resource+source combination that leaves no ambiguity about what the tool does, especially since there are no sibling tools to differentiate from.

    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 parameter explanations (e.g., chain ID examples like 'bsc' and 'eth'), but does not explicitly state when to use this tool versus alternatives. With no sibling tools mentioned, there's no guidance on tool selection, though the parameter details offer some practical context for appropriate usage.

    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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  • Evaluate tool definition quality.

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