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falahgs

MCP CSV Analysis with Gemini AI

by falahgs
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TypeScript
Hybrid

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: analyze-csv focuses on data analysis and insights, generate-thinking produces text-based reasoning, and visualize-data creates charts. There is no overlap in functionality, making tool selection straightforward for an agent.

    Naming Consistency4/5

    The tools follow a consistent verb-object pattern (analyze-csv, generate-thinking, visualize-data), all using kebab-case. The naming is predictable and readable, with only minor deviations like 'visualize-data' using a verb-noun structure while others use verb-ing-noun.

    Tool Count3/5

    With only 3 tools, the server feels thin for a CSV analysis domain that could include operations like data cleaning, filtering, or exporting. While the tools cover core AI-driven tasks, the scope is limited and might require workarounds for common data workflows.

    Completeness2/5

    There are significant gaps in the tool surface for CSV analysis: no tools for basic operations like loading/reading CSV files, filtering data, handling missing values, or exporting results. The server relies heavily on AI and visualization without foundational data manipulation capabilities, which could lead to agent failures in typical data processing tasks.

  • Average 2.9/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 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

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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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'EDA and data science insights' but doesn't specify what the analysis entails, how results are returned (e.g., as text, files, or structured data), or any constraints like file size limits or processing time. For an AI-powered analysis tool with no annotations, this leaves significant gaps in understanding its behavior.

    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 a single, efficient sentence that front-loads the core purpose without unnecessary details. Every word earns its place, making it highly concise and well-structured for quick understanding.

    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 complexity of an AI-powered analysis tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'analysis' outputs look like, how insights are delivered, or any behavioral traits. This is inadequate for a tool that likely produces varied results based on input parameters.

    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?

    Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no additional meaning beyond what's in the schema, such as explaining 'EDA' or 'data science insights' in relation to parameters. Baseline 3 is appropriate when the schema does the heavy lifting, but no extra value is provided.

    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: 'Analyze CSV file using Gemini's AI capabilities for EDA and data science insights.' It specifies the verb ('analyze'), resource ('CSV file'), and technology ('Gemini's AI capabilities'), distinguishing it from sibling tools like 'generate-thinking' and 'visualize-data' which likely serve different functions. However, it doesn't explicitly differentiate from siblings beyond the general domain.

    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 sibling tools, prerequisites, or scenarios where this tool is preferred over others. The only implied usage is for CSV analysis with AI, but this is too vague for effective tool selection.

    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 provided, so the description carries full burden. It mentions the model is 'experimental', hinting at potential instability or variability, but lacks details on rate limits, authentication needs, output format, or error handling. This is inadequate for a tool with no annotation coverage.

    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 a single, efficient sentence with no wasted words. It is front-loaded with the core purpose and includes a key detail (experimental model) without unnecessary elaboration.

    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 no annotations and no output schema, the description is incomplete. It lacks information on behavioral traits, return values, or error handling, which are critical for a generation tool with experimental aspects. The description does not compensate for these gaps.

    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?

    Schema description coverage is 100%, so the schema already documents both parameters (prompt and outputDir). The description adds no additional meaning beyond what the schema provides, such as examples or constraints, meeting the baseline for high coverage.

    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 action ('generate') and resource ('detailed thinking process text'), specifying it uses 'Gemini's experimental thinking model'. It doesn't explicitly differentiate from sibling tools (analyze-csv, visualize-data), but those appear to be unrelated data processing tools, so the purpose is clear without direct comparison.

    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 alternatives or in what context. The description states what it does but offers no usage context, prerequisites, or exclusions, leaving the agent to infer applicability.

    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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'generate visualizations' but doesn't specify what happens (e.g., saves files, displays charts, requires specific permissions, or has rate limits). For a tool with 5 parameters and no annotation coverage, this leaves significant behavioral 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?

    The description is a single, efficient sentence: 'Generate visualizations from CSV data using Chart.js'. It's front-loaded with the core purpose, has zero wasted words, and appropriately sized for the tool's complexity.

    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 5 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., file paths, chart objects, errors) or behavioral aspects like file handling. For a data visualization tool with multiple inputs, more context is needed to guide effective use.

    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?

    Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description adds no additional parameter semantics beyond implying CSV data visualization. It doesn't explain parameter interactions or provide context beyond what's in the schema, meeting the baseline for high coverage.

    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: 'Generate visualizations from CSV data using Chart.js'. It specifies the action (generate visualizations), resource (CSV data), and technology (Chart.js). However, it doesn't explicitly differentiate from sibling tools like 'analyze-csv' or 'generate-thinking', which might have overlapping data processing functions.

    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 sibling tools like 'analyze-csv' or 'generate-thinking', nor does it specify scenarios where visualization is preferred over other data processing methods. The user must infer usage from the purpose alone.

    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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