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sohamkamat28

Ripplecheck MCP Server

by sohamkamat28
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Python
Hybrid

Server Quality Checklist

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

  • Latest release: v2.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: assess_schema_change performs a complex analysis, while list_demo_scenarios provides test scenarios. No overlap or ambiguity exists.

    Naming Consistency5/5

    Both tools follow the same verb_noun pattern (assess_schema_change, list_demo_scenarios), making the API predictable and easy to navigate.

    Tool Count3/5

    With only two tools, the server feels minimally scoped. While appropriate for a niche utility, the count is on the thin side, making it harder to justify a standalone server.

    Completeness3/5

    The core assessment functionality is present, but the server lacks any management operations (e.g., listing past assessments, retrieving specific results), which limits its coverage of the full workflow.

  • Average 3.3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 4 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under Apache 2.0.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of disclosing behavioral traits. It does not mention the writeback parameter's default of true and its side effect of appending a hash-sealed capsule to a DataHub column, nor does it clarify whether the operation is read-only, requires permissions, or returns a specific format. The word 'counterfactual' hints at hypothetical analysis but is not explicit about side effects.

    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 a single sentence that immediately opens with the verb 'Compile', making the primary action front-loaded. It is free of filler, though the dense list of outputs is jargon-heavy. Still, it is efficiently packed with useful information.

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

    Completeness2/5

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

    This is a complex tool producing multiple artifacts, yet there are no annotations and no output schema. The one-line description does not explain operational context, side effects, when to use it, or any caveats. It also fails to mention the writeback behavior, which is a significant gap for a tool that appears to perform an assessment but may also mutate DataHub.

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

    Parameters3/5

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

    The input schema already describes both parameters (change, writeback) with 100% coverage. The description adds context that 'change' accepts DDL or natural language, which aligns with the schema example, but it adds no additional meaning for 'writeback' beyond the schema. Baseline of 3 is appropriate since the schema handles the heavy lifting.

    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 uses a specific verb 'compile' with a clear resource ('proposed DDL or a column change') and enumerates concrete outputs (counterfactual DataHub graph, policy proof, release gate, migration DAG, evidence manifest). This makes the tool's function unmistakable and fully distinguishes it from the sibling 'list_demo_scenarios'.

    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?

    There is no guidance on when to use this tool, no mention of prerequisites, and no comparison with alternatives. The description only states what it does, not in what situations it should be selected over other tools like list_demo_scenarios.

    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?

    With no annotations, the description carries the burden. It implies a read-only operation ('List') and notes scenarios are 'offline', but does not disclose return format, side effects, or any caveats. For a simple list tool, it is minimally adequate but lacks depth.

    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?

    A single, front-loaded sentence with no filler. It states the action and the object clearly and economically.

    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?

    For a zero-parameter, no-output-schema list tool with a sibling, the description provides the core purpose and expected content. It is slightly vague about the format of 'expected decisions,' but overall it is reasonably complete for the tool's simplicity.

    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 zero parameters, so schema coverage is complete at 100%. The description does not need to elaborate on parameters; the baseline of 4 applies given the absence of parameters and no contradictions.

    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?

    Uses a specific verb ('List') and identifies the resource ('built-in offline scenarios'), and adds the output dimension ('expected decisions'). It is clear on its own, though it does not explicitly differentiate from the sibling tool 'assess_schema_change'.

    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 on when to use this tool versus alternatives. The presence of a sibling tool ('assess_schema_change') makes the absence of explicit usage context more noticeable.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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