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Education Data MCP Server

Name: Education Data MCP Server
Author: ckz

by ckz

Overview Schema Source Related Servers Discussions Admin

JavaScript

Remote

Servidor MCP de datos educativos

Este repositorio contiene un servidor MCP (Protocolo de Contexto de Modelo) que proporciona acceso a la API de Datos Educativos del Urban Institute. El servidor está diseñado para usarse con Claude y facilitar el acceso a los datos educativos.

Estructura del repositorio

education-data-package-r/ : El paquete R original para acceder a la API de datos educativos (como referencia)
src/ : El código fuente del servidor MCP
build/ : El servidor MCP compilado

Related MCP server: Slate MCP Server

Acerca de la API de datos educativos

La API de datos educativos del Urban Institute proporciona acceso a una amplia gama de datos educativos, incluidos:

Datos de matriculación de escuelas y distritos
Datos de colegios y universidades
Datos de evaluación
Datos financieros
Y mucho más

La API está organizada por niveles (escuelas, distritos escolares, colegio-universidad), fuentes (ccd, ipeds, crdc, etc.) y temas (inscripción, directorio, finanzas, etc.).

Características

Recupere datos educativos detallados a través de la herramienta get_education_data
Recupere datos educativos agregados mediante la herramienta get_education_data_summary
Explorar los puntos finales disponibles a través de los recursos

Instalación

Clonar este repositorio:

git clone https://github.com/yourusername/edu-data-mcp-server.git
cd edu-data-mcp-server

Instalar dependencias:
```
npm install
```
Construir el servidor:
```
npm run build
```
Hacer que el servidor esté disponible para npx:
```
npm link
```

Configuración del servidor MCP

Para utilizar este servidor MCP con Claude, debe agregarlo a su archivo de configuración de MCP.

Para la aplicación de escritorio de Claude (macOS)

Editar ~/Library/Application Support/Claude/claude_desktop_config.json :

{
  "mcpServers": {
    "edu-data": {
      "command": "npx",
      "args": ["edu-data-mcp-server"],
      "disabled": false,
      "alwaysAllow": []
    }
  }
}

Para Claude en VSCode

Editar /home/codespace/.vscode-remote/data/User/globalStorage/rooveterinaryinc.roo-cline/settings/cline_mcp_settings.json :

{
  "mcpServers": {
    "edu-data": {
      "command": "npx",
      "args": ["edu-data-mcp-server"],
      "disabled": false,
      "alwaysAllow": []
    }
  }
}

Herramientas disponibles

obtener datos educativos

Recupera datos educativos detallados de la API.

Parámetros:

level (obligatorio): nivel de datos de la API para consultar (por ejemplo, 'escuelas', 'distritos escolares', 'universidad')
source (obligatoria): fuente de datos API para consultar (por ejemplo, 'ccd', 'ipeds', 'crdc')
topic (obligatorio): tema de datos de la API para consultar (por ejemplo, 'inscripción', 'directorio')
subtopic (opcional): Lista de parámetros de agrupación (por ejemplo, ['raza', 'sexo'])
filters (opcionales): filtros de consulta (por ejemplo, {año: 2008, grado: [9,10,11,12]})
add_labels (opcional): agrega etiquetas de variables cuando corresponda (predeterminado: falso)
limit (opcional): Limita el número de resultados (predeterminado: 100)

Ejemplo:

{
  "level": "schools",
  "source": "ccd",
  "topic": "enrollment",
  "subtopic": ["race", "sex"],
  "filters": {
    "year": 2008,
    "grade": [9, 10, 11, 12]
  },
  "add_labels": true,
  "limit": 50
}

obtener_resumen_de_datos_educativos

Recupera datos educativos agregados de la API.

Parámetros:

level (obligatorio): Nivel de datos de la API a consultar
source (obligatoria): fuente de datos API para consultar
topic (obligatorio): tema de datos de la API para consultar
subtopic (opcional): Parámetros adicionales (solo aplicables a ciertos puntos finales)
stat (obligatorio): Estadística de resumen a calcular (por ejemplo, 'suma', 'promedio', 'conteo', 'mediana')
var (obligatorio): Variable a resumir
by (obligatorio): Variables para agrupar los resultados
filters (opcional): filtros de consulta

Ejemplo:

{
  "level": "schools",
  "source": "ccd",
  "topic": "enrollment",
  "stat": "sum",
  "var": "enrollment",
  "by": ["fips"],
  "filters": {
    "fips": [6, 7, 8],
    "year": [2004, 2005]
  }
}

Recursos disponibles

El servidor proporciona recursos para explorar los puntos finales disponibles:

edu-data://endpoints/{level}/{source}/{topic} : Información sobre un punto final de datos educativos específico

Ejemplo de uso con Claude

Una vez configurado el servidor MCP, puedes usarlo con Claude para acceder a los datos educativos:

Can you show me the enrollment data for high schools in California for 2020?

Luego, Claude puede usar el servidor MCP para recuperar y analizar los datos:

use_mcp_tool
server_name: edu-data
tool_name: get_education_data
arguments: {
  "level": "schools",
  "source": "ccd",
  "topic": "enrollment",
  "filters": {
    "year": 2020,
    "fips": 6,
    "grade": [9, 10, 11, 12]
  },
  "limit": 10
}

Desarrollo

Para ejecutar el servidor directamente:

npm start

Para ejecutar el servidor en modo de observación durante el desarrollo:

npm run watch

Para inspeccionar las capacidades del servidor:

npm run inspector

Para ejecutar el servidor usando npx:

npx edu-data-mcp-server

Licencia

Instituto Tecnológico de Massachusetts (MIT)

Available Tools

2 tools

get_education_dataB

Retrieve education data from the Urban Institute's Education Data API

ParametersJSON Schema

Name	Required	Description
`level`	Yes	API data level to query (e.g., 'schools', 'school-districts', 'college-university')
`source`	Yes	API data source to query (e.g., 'ccd', 'ipeds', 'crdc')
`topic`	Yes	API data topic to query (e.g., 'enrollment', 'directory')
`subtopic`	No	Optional list of grouping parameters (e.g., ['race', 'sex'])
`filters`	No	Optional query filters (e.g., {year: 2008, grade: [9,10,11,12]})
`add_labels`	No	Add variable labels when applicable (default: false)
`limit`	No	Limit the number of results (default: 100)

TDQS

B3.1/5.0

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 states this is a retrieval operation but doesn't mention whether it's read-only, has rate limits, requires authentication, returns paginated results, or handles errors. For a data API tool with 7 parameters, 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 that states exactly what the tool does without unnecessary words. It's appropriately sized for a data retrieval tool and front-loads the core functionality.

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 complexity (7 parameters, no output schema, no annotations), the description is minimally adequate but leaves significant gaps. It identifies the data source but doesn't explain return formats, error handling, or how to interpret results. The combination of good schema coverage but missing behavioral context results in a borderline complete picture.

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 schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description doesn't add any additional parameter semantics beyond what's in the schema, such as explaining relationships between parameters or providing usage examples. This meets the baseline expectation when schema coverage is complete.

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 'retrieve' and resource 'education data' with specific source 'Urban Institute's Education Data API', making the purpose unambiguous. However, it doesn't distinguish this from its sibling tool 'get_education_data_summary', which likely provides aggregated or summarized data versus the raw retrieval described here.

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, including its sibling 'get_education_data_summary'. There's no mention of prerequisites, typical use cases, or contextual factors that would help an agent decide between this and other data retrieval options.

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

get_education_data_summaryC

Retrieve aggregated education data from the Urban Institute's Education Data API

ParametersJSON Schema

Name	Required	Description
`level`	Yes	API data level to query
`source`	Yes	API data source to query
`topic`	Yes	API data topic to query
`subtopic`	No	Optional additional parameters (only applicable to certain endpoints)
`stat`	Yes	Summary statistic to calculate (e.g., 'sum', 'avg', 'count', 'median')
`var`	Yes	Variable to be summarized
`by`	Yes	Variables to group results by
`filters`	No	Optional query filters

TDQS

C2.9/5.0

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 states the tool retrieves aggregated data but doesn't mention critical behavioral aspects like whether it's read-only, potential rate limits, authentication requirements, error handling, or the format/scope of returned data. This leaves significant gaps for a tool with 8 parameters.

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's appropriately sized and front-loaded with the core functionality, making it easy to parse quickly.

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?

For a tool with 8 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what 'aggregated' means in practice, doesn't address the sibling tool relationship, and provides no behavioral context. The agent would struggle to use this tool effectively without additional information.

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 schema description coverage is 100%, so all parameters are documented in the schema. The description adds no additional parameter semantics beyond implying aggregation occurs, which is already clear from the schema's parameter descriptions (e.g., 'stat' for summary statistics). This meets the baseline for high schema 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 ('Retrieve aggregated education data') and the source ('Urban Institute's Education Data API'), which is specific and informative. However, it doesn't explicitly distinguish this tool from its sibling 'get_education_data', leaving some ambiguity about when to use one versus the other.

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 its sibling 'get_education_data' or any alternatives. It lacks context about appropriate use cases, prerequisites, or exclusions, leaving the agent with no usage direction beyond the basic purpose.

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.

2 tool updatesAug 15, 2025
- First observedget_education_data
- First observedget_education_data_summary

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

C2.9/5.0

Scored 2026-04-18 11:00 across 2 tools

Disambiguation2/5

The two tools have overlapping purposes that could easily cause confusion. Both retrieve education data from the same API, with 'get_education_data_summary' described as aggregated data, but the distinction between regular and aggregated data is not clearly defined in the descriptions. An agent might struggle to choose between them without more specific guidance on when to use each.

Naming Consistency5/5

The tool names follow a perfectly consistent verb_noun pattern with 'get_education_data' and 'get_education_data_summary'. Both use snake_case and the same verb 'get', making them predictable and easy to parse. There are no deviations or mixed conventions in the naming.

Tool Count2/5

With only 2 tools, this server feels too thin for its apparent scope of accessing an education data API. A typical data API server would benefit from more operations like filtering, searching, or accessing different endpoints, making this set under-scoped. The count is borderline minimal and may limit agent functionality.

Completeness2/5

The tool surface is severely incomplete for an education data API domain. There are obvious gaps: no tools for filtering data by parameters, accessing specific datasets, updating or managing data, or handling errors. This forces agents into dead ends and limits practical use to basic retrieval only.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

Reddit Discussion

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MIT

Servidor MCP de datos educativos

Estructura del repositorio

Acerca de la API de datos educativos

Características

Instalación

Configuración del servidor MCP

Para la aplicación de escritorio de Claude (macOS)

Para Claude en VSCode

Herramientas disponibles

obtener datos educativos

obtener_resumen_de_datos_educativos

Recursos disponibles

Ejemplo de uso con Claude

Desarrollo

Licencia

Available Tools

TDQS

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

DatahubMCP

Slate MCP Server

Canvas MCP Server

CampusIntel MCP