Skip to main content
Glama
enesjakozh
tofunori

Claude MCP Data Explorer

by tofunori
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Explorador de datos de Claude MCP para Windows

Implementación en TypeScript de un servidor de Protocolo de Contexto de Modelo (MCP) para la exploración de datos con Claude. Este servidor se integra con Claude Desktop y permite el análisis avanzado de datos, proporcionando herramientas para cargar archivos CSV y ejecutar scripts de análisis de datos en JavaScript.

Prerrequisitos

Related MCP server: mcp-csv-analyst

Instalación (actualizada para Windows)

  1. Clonar este repositorio

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

  2. Instalar dependencias

    npm install

  3. Crear y ejecutar el script de configuración

    npm run setup

    Esto hará lo siguiente:

    • Construya el código TypeScript en JavaScript

    • Configurar Claude Desktop para utilizar el JavaScript compilado

    • Crear directorios necesarios

  4. Reinicie Claude Desktop y habilite el modo de desarrollador

    • Cerrar Claude Desktop por completo

    • Iniciar Claude Desktop

    • Vaya a Ayuda → Habilitar modo de desarrollador

Pruebas manuales

Puede probar el servidor directamente ejecutando:

npm run build
npm run start

El servidor debería iniciarse sin errores. Si lo logra, Claude Desktop también debería poder usar el servidor.

Cómo funciona

Este servidor MCP proporciona dos herramientas principales para Claude:

  1. load-csv : carga datos CSV en la memoria para su análisis

  2. run-script : ejecuta código JavaScript para el procesamiento y análisis de datos.

También incluye una plantilla de indicaciones que guía a Claude a través de un proceso de exploración de datos estructurados.

Uso

  1. Iniciar Claude Desktop

  2. Seleccione la plantilla de solicitud "Explorar datos"

    • Este mensaje aparecerá en Claude Desktop después de la configuración

  3. Ingrese la ruta del archivo CSV y el tema de exploración

    • Ruta de archivo de ejemplo: C:/Users/YourName/Documents/data.csv

    • Tema de ejemplo: "Tendencias de ventas por región"

  4. Deja que Claude analice tus datos

    • Claude cargará el archivo CSV y generará información automáticamente.

    • El servidor maneja archivos grandes de manera eficiente mediante fragmentación

Solución de problemas

  1. Claude no muestra el servidor MCP

    • Verifique el archivo de configuración en %APPDATA%\Claude\claude_desktop_config.json

    • Debe apuntar al archivo JavaScript compilado en el directorio dist

    • Intente reconstruir el proyecto con npm run build

    • Habilite el modo de desarrollador y verifique el archivo de registro de MCP (Desarrollador → Abrir archivo de registro de MCP)

    • Utilice Desarrollador → Recargar todos los servidores MCP para forzar la actualización

  2. Errores de permisos al leer archivos

    • Asegúrese de que Claude tenga acceso a la ubicación del archivo CSV

    • Intente utilizar rutas absolutas con barras diagonales ( / ) o barras diagonales invertidas ( \\ )

  3. Errores de JavaScript en scripts

    • Comprueba que tu script sea compatible con los módulos permitidos

    • Revise los mensajes de error en la respuesta de Claude.

Licencia

Licencia MIT: consulte el archivo LICENCIA para obtener más detalles.

Expresiones de gratitud

  • Basado en el SDK oficial de TypeScript MCP de Anthropic

  • Gracias a la comunidad MCP por los ejemplos y la inspiración.

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

    seraphinerenard
    A
    license
    A
    quality
    Not graded
    maintenance
    An MCP server that enables AI assistants to load, query, and analyze local CSV files using tools for filtering, aggregation, and grouping. It provides capabilities to describe schemas, calculate statistics, and sample data directly from CSV files.
    6
    -

  • csv-analyzer

    viajeraune
    F
    license
    Not graded
    quality
    D
    maintenance
    A local MCP server for analyzing CSV files from your filesystem, particularly suited for chatbot conversation logs. Allows listing, reading, filtering, merging, and statistical analysis of CSV data via natural language.
    -

  • data-explore

    Mingwei2
    F
    license
    A
    quality
    D
    maintenance
    An MCP server for dataset exploration and analysis, enabling LLM clients to perform summary, correlation, distribution, missing value analysis, data cleaning, and statistical tests directly on CSV files.
    3
    -

Appeared in Searches