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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

教育データMCPサーバー

このリポジトリには、Urban Instituteの教育データAPIへのアクセスを提供するMCP（Model Context Protocol）サーバーが含まれています。このサーバーは、Claudeと組み合わせて使用することで、教育データへの容易なアクセスを可能にするように設計されています。

リポジトリ構造

education-data-package-r/ : Education Data API にアクセスするためのオリジナルの R パッケージ (参考用)
src/ : MCPサーバーのソースコード
build/ : コンパイルされたMCPサーバー

Related MCP server: Slate MCP Server

教育データAPIについて

Urban Institute の教育データ API は、次のような幅広い教育データへのアクセスを提供します。

学校および学区の入学データ
大学のデータ
評価データ
財務データ
その他にも

API は、レベル (学校、学区、大学)、ソース (ccd、ipeds、crdc など)、トピック (登録、ディレクトリ、財務など) 別に整理されています。

特徴

get_education_dataツールを使用して詳細な教育データを取得します
get_education_data_summaryツールを使用して集計された教育データを取得します。
リソース経由で利用可能なエンドポイントを参照する

インストール

このリポジトリをクローンします:

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

依存関係をインストールします:
```
npm install
```
サーバーを構築します。
```
npm run build
```
サーバーを npx で利用できるようにします。
```
npm link
```

MCP サーバーの設定

この MCP サーバーを Claude で使用するには、MCP 設定構成ファイルに追加する必要があります。

Claude デスクトップアプリ (macOS)

~/Library/Application Support/Claude/claude_desktop_config.jsonを編集します。

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

VSCodeのClaude向け

/home/codespace/.vscode-remote/data/User/globalStorage/rooveterinaryinc.roo-cline/settings/cline_mcp_settings.jsonを編集します。

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

利用可能なツール

教育データを取得する

API から詳細な教育データを取得します。

パラメータ:

level （必須）: クエリする API データレベル（例: 'schools'、'school-districts'、'college-university'）
source （必須）: クエリする API データソース（例: 'ccd'、'ipeds'、'crdc'）
topic (必須): クエリする API データトピック (例: 'enrollment'、'directory')
subtopic （オプション）：グループ化パラメータのリスト（例：['人種', '性別']）
filters （オプション）：クエリフィルター（例：{year: 2008, grade: [9,10,11,12]}）
add_labels (オプション): 該当する場合は変数ラベルを追加します (デフォルト: false)
limit (オプション): 結果の数を制限する (デフォルト: 100)

例：

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

教育データの概要を取得する

API から集計された教育データを取得します。

パラメータ:

level （必須）: クエリするAPIデータレベル
source （必須）: クエリする API データソース
topic (必須): クエリする API データトピック
subtopic （オプション）：追加パラメータ（特定のエンドポイントにのみ適用可能）
stat (必須): 計算する要約統計値 (例: 'sum'、'avg'、'count'、'median')
var (必須): 集計する変数
by (必須): 結果をグループ化する変数
filters （オプション）: クエリフィルター

例：

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

利用可能なリソース

サーバーは、利用可能なエンドポイントを参照するためのリソースを提供します。

edu-data://endpoints/{level}/{source}/{topic} : 特定の教育データエンドポイントに関する情報

クロードとの使用例

MCP サーバーが設定されると、Claude で使用して教育データにアクセスできます。

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

その後、クロードは MCP サーバーを使用してデータを取得および分析できます。

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
}

発達

サーバーを直接実行するには:

npm start

開発中にサーバーをウォッチモードで実行するには:

npm run watch

サーバーの機能を検査するには:

npm run inspector

npx を使用してサーバーを実行するには:

npx edu-data-mcp-server

ライセンス

マサチューセッツ工科大学

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

教育データMCPサーバー

リポジトリ構造

教育データAPIについて

特徴

インストール

MCP サーバーの設定

Claude デスクトップ アプリ (macOS)

VSCodeのClaude向け

利用可能なツール

教育データを取得する

教育データの概要を取得する

利用可能なリソース

クロードとの使用例

発達

ライセンス

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

Claude デスクトップアプリ (macOS)