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kukapay

etf-flow-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

    With only one tool, there is no possibility of confusion or overlap between tools. The tool's purpose is singular and clearly defined.

    Naming Consistency5/5

    The single tool name 'get_etf_flow' follows a clear verb_noun pattern. Since there are no other tools, consistency is inherently perfect.

    Tool Count2/5

    One tool is too few for the server's apparent scope of ETF flow data analysis. It lacks complementary tools like historical trends, comparisons, or metadata, making the surface feel thin and incomplete for the domain.

    Completeness2/5

    The tool set is severely incomplete for ETF flow analysis. It only fetches data for BTC or ETH, missing operations like multi-coin queries, date range filtering, summary statistics, or visualization, which are essential for comprehensive coverage.

  • Average 4.1/5 across 1 of 1 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 action ('Fetch') and output format ('Markdown table'), but lacks details on error handling, rate limits, authentication needs, or data freshness. It adequately covers basic behavior but misses advanced operational context.

    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 a clear opening sentence followed by specific sections for parameters and returns. Every sentence adds value without redundancy, making it efficient and easy to parse.

    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 (single parameter, no output schema, no annotations), the description is mostly complete. It covers purpose, parameters, and return format, but could improve by addressing behavioral aspects like error cases or data limitations, which would enhance completeness for an API-based tool.

    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% coverage. It explicitly defines the 'coin' parameter as a string with allowed values ('BTC' or 'ETH') and explains its purpose ('Cryptocurrency to query'), compensating fully 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 tool's purpose with a specific verb ('Fetch'), resource ('historical ETF flow data'), and scope ('for BTC or ETH from CoinGlass API'). It distinguishes the data source and format, making the function unambiguous even without sibling tools.

    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 specifying the cryptocurrency options ('BTC' or 'ETH') and the data source (CoinGlass API), but it does not provide explicit guidance on when to use this tool versus alternatives or any prerequisites. Since there are no sibling tools, the lack of comparative guidance is less critical.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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