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Claude MCP Data Explorer

by tofunori
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Claude MCP Data Explorer for Windows

A TypeScript implementation of a Model Context Protocol (MCP) server for data exploration with Claude. This server integrates with Claude Desktop and enables advanced data analysis by providing tools to load CSV files and execute JavaScript data analysis scripts.

Prerequisites

Related MCP server: mcp-csv-analyst

Installation (Updated for Windows)

  1. Clone this repository

    git clone https://github.com/tofunori/claude-mcp-data-explorer.git
cd claude-mcp-data-explorer

  2. Install dependencies

    npm install

  3. Build and run setup script

    npm run setup

    This will:

    • Build the TypeScript code to JavaScript

    • Configure Claude Desktop to use the compiled JavaScript

    • Create necessary directories

  4. Restart Claude Desktop and enable Developer Mode

    • Close Claude Desktop completely

    • Start Claude Desktop

    • Go to Help → Enable Developer Mode

Manual Testing

You can test the server directly by running:

npm run build
npm run start

The server should start without errors. If you can run this successfully, Claude Desktop should be able to use the server as well.

How It Works

This MCP server provides two main tools for Claude:

  1. load-csv - Loads CSV data into memory for analysis

  2. run-script - Executes JavaScript code for data processing and analysis

It also includes a prompt template that guides Claude through a structured data exploration process.

Usage

  1. Start Claude Desktop

  2. Select the "Explore Data" prompt template

    • This prompt will appear in Claude Desktop after setup

  3. Enter CSV file path and exploration topic

    • Example file path: C:/Users/YourName/Documents/data.csv

    • Example topic: "Sales trends by region"

  4. Let Claude analyze your data

    • Claude will load the CSV file and generate insights automatically

    • The server handles large files efficiently using chunking

Troubleshooting

  1. Claude doesn't show the MCP server

    • Check the configuration file at %APPDATA%\Claude\claude_desktop_config.json

    • It should point to the compiled JavaScript file in the dist directory

    • Try rebuilding the project with npm run build

    • Enable Developer Mode and check the MCP Log File (Developer → Open MCP Log File)

    • Use Developer → Reload All MCP Servers to force refresh

  2. Permission errors reading files

    • Make sure Claude has access to the CSV file location

    • Try using absolute paths with forward slashes (/) or escaped backslashes (\\)

  3. JavaScript errors in scripts

    • Check that your script is compatible with the allowed modules

    • Review any error messages in Claude's response

License

MIT License - see LICENSE file for details.

Acknowledgments

  • Based on the official MCP TypeScript SDK from Anthropic

  • Thanks to the MCP community for examples and inspiration

Available Tools

2 tools
load-csvC

Load a CSV file into a DataFrame for analysis

ParametersJSON Schema
NameRequiredDescriptionDefault
csv_pathYesPath to the CSV file to load
df_nameNoName for the DataFrame (optional, defaults to df_1, df_2, etc.)

TDQS

C2.9/5.0
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 states the action 'load a CSV file into a DataFrame' but lacks details on permissions needed, error handling (e.g., invalid paths), rate limits, or what happens after loading (e.g., memory usage, persistence). For a tool with no annotation coverage, this is a significant gap.

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 action ('Load a CSV file') and purpose ('for analysis'). There is zero waste, 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 no annotations, no output schema, and a tool that performs data loading (a potentially complex operation with file I/O), the description is incomplete. It doesn't cover behavioral aspects like error conditions, return values, or dependencies, leaving gaps for an AI agent to use it correctly in varied contexts.

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 (csv_path and df_name) with clear descriptions. The description adds no additional meaning beyond implying CSV loading for analysis, which aligns with the schema but doesn't provide extra syntax or format details. Baseline 3 is appropriate when the schema does the heavy lifting.

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 verb 'load' and resource 'CSV file', specifying it's for analysis via a DataFrame. It distinguishes from the sibling 'run-script' by focusing on data loading rather than script execution. However, it doesn't explicitly differentiate from potential other data loading tools (none listed), keeping it at 4.

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. The description implies it's for loading CSV files into DataFrames, but there's no mention of prerequisites (e.g., file accessibility), when not to use it (e.g., for non-CSV files), or alternatives like 'run-script' for other data processing. This leaves usage context vague.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

run-scriptC

Execute a JavaScript script for data analysis and visualization

ParametersJSON Schema
NameRequiredDescriptionDefault
scriptYesJavaScript script to execute

TDQS

C2.9/5.0
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 states the tool executes a JavaScript script but doesn't describe safety aspects (e.g., sandboxing, permissions), performance traits (e.g., execution time limits, resource usage), or what happens upon execution (e.g., output format, side effects). For a tool that runs arbitrary code with no annotation coverage, this is a significant gap.

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 directly states the tool's purpose without unnecessary words. It is appropriately sized and front-loaded, with every part contributing essential information (verb, resource, domain).

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 executing arbitrary JavaScript code, the lack of annotations, and no output schema, the description is incomplete. It doesn't address critical aspects like security implications, error handling, or what the tool returns (e.g., visualization output, analysis results). For a tool with such potential impact, more context is needed.

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 has 100% description coverage, with one parameter 'script' fully documented in the schema. The description adds no additional meaning about parameters beyond what the schema provides (e.g., no examples of script content, no constraints on JavaScript features). With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't detract either.

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 with a specific verb ('Execute') and resource ('JavaScript script'), and specifies the domain ('for data analysis and visualization'). It doesn't distinguish from the sibling tool 'load-csv', which appears to be a different operation, so it doesn't explicitly differentiate from siblings.

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 mentions the domain (data analysis and visualization) but doesn't specify prerequisites, limitations, or when not to use it. There's no explicit comparison with the sibling tool 'load-csv' or other potential tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 2 tool updates
    • First observedload-csv
    • First observedrun-script
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: one loads CSV data into a DataFrame, while the other executes JavaScript scripts for analysis and visualization. There is no overlap or ambiguity between these functions, making it easy for an agent to select the correct tool.

Naming Consistency4/5

Both tools use a verb-noun pattern (load-csv, run-script), which is consistent and readable. The hyphenated style is maintained throughout, though the specific convention (hyphens vs. underscores) is less important than the consistency, which is good here with only minor deviations from common patterns.

Tool Count2/5

With only two tools, the server feels thin for a 'Data Explorer' purpose, as it lacks essential operations like data querying, filtering, transformation, or exporting. While the tools are functional, the count is too low to adequately cover the expected scope of data exploration and analysis.

Completeness2/5

For a data exploration server, there are significant gaps: no tools for querying data, filtering, aggregating, visualizing beyond scripts, or exporting results. The surface is severely incomplete, as agents cannot perform basic data exploration tasks without relying heavily on external scripts, leading to potential failures in common workflows.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources

Related MCP Connectors

Related MCP Servers

  • mcp-csv-analyst

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  • csv-analyzer

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  • data-explore

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