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sindhug

MCP DataFrame QA

by sindhug
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Python
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Server Quality Checklist

58%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: executing a plan, previewing data, and answering natural language queries. There is no overlap or ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern with snake_case, starting with a verb and clearly indicating the action.

    Tool Count4/5

    With only 3 tools, the server is somewhat minimal but still reasonable for focused dataframe QA. The count is on the lower end but not inappropriate.

    Completeness3/5

    The tools cover execution of analysis plans, preview, and query, but lack basic data exploration features like listing columns or summary statistics, which are notable gaps for a QA server.

  • Average 3.1/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 29 commits in the last 12 weeks
    • Last stable release on
    • 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.

  • 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": [
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  ]
}

    Then . Browse examples.

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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

  • Behavior2/5

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

    The description claims the tool is read-only but provides no other behavioral details. With no annotations, the description should disclose potential side effects, authorization needs, or error handling, but it fails to do so.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is extremely concise (one sentence) and front-loaded with purpose, but it sacrifices too much essential information. While efficient, it fails to fully earn its place by omitting critical details.

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

    Completeness1/5

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

    Given the presence of a nested object parameter and an output schema, the description is severely incomplete. It does not explain what constitutes a validated plan, what the output looks like, or how to obtain a plan. Sibling tools are listed but not contextualized.

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

    Parameters1/5

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

    The input schema has 0% description coverage, and the description adds no information about the parameters (plan or dataset_id). The agent cannot understand what valid input looks like or how to construct the plan.

    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 executes a validated, read-only dataframe analysis plan. It uses a specific verb and resource, and the read-only constraint distinguishes it from sibling tools like preview_dataframe and query_dataframe.

    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 its siblings, nor are prerequisites (e.g., having a validated plan) mentioned. The description lacks 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.

  • Behavior2/5

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

    No annotations are present, so the description must convey behavior. It only says 'capped preview' but fails to specify details like read-only nature, data source, or what 'capped' means exactly (e.g., first N rows).

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is extremely concise (one sentence) with no wasted words, but it lacks structure and front-loading of key information. Minimalism here comes at the cost of completeness.

    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?

    Given the existence of an output schema and only two parameters, the tool is simple, but the description still fails to explain parameter meanings, usage context, or return value nature, making it incomplete.

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

    Parameters1/5

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

    Schema description coverage is 0%, and the description adds no clarification for the parameters 'limit' and 'dataset_id'. Their purpose is left entirely to naming conventions.

    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 a specific verb ('Return') and resource ('capped preview of the dataframe'), differentiating it from sibling tools like 'execute_analysis_plan' and 'query_dataframe'.

    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 the siblings. There is no mention of when a preview is appropriate or when to use query instead.

    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 must carry the burden. It mentions using a 'built-in conservative planner,' which hints at cautious behavior, but does not disclose other traits like read-only nature, error handling, or rate limits. The output schema exists but is not described. Adequate but leaves gaps.

    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?

    Two sentences with no wasted words. The primary action is front-loaded in the first sentence, and the second provides a crucial usage hint. Excellent efficiency.

    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 and the presence of an output schema, the description covers the core purpose and usage. However, it omits details about parameter semantics, possible question types, and behavioral nuances, leaving some gaps for an AI agent.

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

    Parameters2/5

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

    Schema description coverage is 0%, so the description should compensate, but it does not explain the parameters ('question', 'dataset_id'). While parameter names are somewhat self-explanatory, the description adds no further meaning, formatting hints, or examples. Below the baseline of 3.

    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: 'Answer a natural-language question about the dataframe.' It also distinguishes itself from sibling tools by mentioning that for complex analysis, one should use execute_analysis_plan.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines5/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description explicitly advises when to use this tool (simple natural-language questions) and when not (complex analysis), naming the alternative: 'call execute_analysis_plan with a typed plan.'

    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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