Which integrations are available for this server?

Provides repository hosting and cloning capabilities for the MCP server code and documentation. Supports configuration within the Claude Desktop App on macOS through editing the claude_desktop_config.json file. Enables installation, building, and execution of the MCP server through npm commands and package management.

How do I use Education Data MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Education Data MCP Server show me high school enrollment in California for 2020" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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Education Data MCP Server

by ckz

Overview Schema Related Servers Score Discussions

JavaScript

Remote

Education Data MCP Server

This repository contains an MCP (Model Context Protocol) server that provides access to the Urban Institute's Education Data API. The server is designed to be used with Claude to enable easy access to education data.

Repository Structure

education-data-package-r/: The original R package for accessing the Education Data API (for reference)
src/: The MCP server source code
build/: The compiled MCP server

Related MCP server: Slate MCP Server

About the Education Data API

The Urban Institute's Education Data API provides access to a wide range of education data, including:

School and district enrollment data
College and university data
Assessment data
Financial data
And much more

The API is organized by levels (schools, school-districts, college-university), sources (ccd, ipeds, crdc, etc.), and topics (enrollment, directory, finance, etc.).

Features

Retrieve detailed education data via the get_education_data tool
Retrieve aggregated education data via the get_education_data_summary tool
Browse available endpoints via resources

Installation

Clone this repository:

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

Install dependencies:
```
npm install
```
Build the server:
```
npm run build
```
Make the server available for npx:
```
npm link
```

Configuring the MCP Server

To use this MCP server with Claude, you need to add it to your MCP settings configuration file.

For Claude Desktop App (macOS)

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

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

For Claude in VSCode

Edit /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": []
    }
  }
}

Available Tools

get_education_data

Retrieves detailed education data from the API.

Parameters:

level (required): API data level to query (e.g., 'schools', 'school-districts', 'college-university')
source (required): API data source to query (e.g., 'ccd', 'ipeds', 'crdc')
topic (required): API data topic to query (e.g., 'enrollment', 'directory')
subtopic (optional): List of grouping parameters (e.g., ['race', 'sex'])
filters (optional): Query filters (e.g., {year: 2008, grade: [9,10,11,12]})
add_labels (optional): Add variable labels when applicable (default: false)
limit (optional): Limit the number of results (default: 100)

Example:

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

get_education_data_summary

Retrieves aggregated education data from the API.

Parameters:

level (required): API data level to query
source (required): API data source to query
topic (required): API data topic to query
subtopic (optional): Additional parameters (only applicable to certain endpoints)
stat (required): Summary statistic to calculate (e.g., 'sum', 'avg', 'count', 'median')
var (required): Variable to be summarized
by (required): Variables to group results by
filters (optional): Query filters

Example:

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

Available Resources

The server provides resources for browsing available endpoints:

edu-data://endpoints/{level}/{source}/{topic}: Information about a specific education data endpoint

Example Usage with Claude

Once the MCP server is configured, you can use it with Claude to access education data:

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

Claude can then use the MCP server to retrieve and analyze the data:

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
}

Development

To run the server directly:

npm start

To run the server in watch mode during development:

npm run watch

To inspect the server's capabilities:

npm run inspector

To run the server using npx:

npx edu-data-mcp-server

License

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. Dates show when Glama detected each change.

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

Install Server

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TDQS

C2.9/5.0

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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Education Data MCP Server

Repository Structure

About the Education Data API

Features

Installation

Configuring the MCP Server

For Claude Desktop App (macOS)

For Claude in VSCode

Available Tools

get_education_data

get_education_data_summary

Available Resources

Example Usage with Claude

Development

License

Available Tools

TDQS

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

DatahubMCP

Slate MCP Server

global-education-mcp

Canvas MCP Server

Latest Blog Posts

MCP directory API