F
licenseNot graded
qualityD
maintenanceEnables Claude to query MySQL databases and access Google Workspace (Sheets, Forms, Drive) for education program management, with built-in templates for data analysis and report generation.
5
-
Education Data MCP Server
by ckz
교육 데이터 MCP 서버
이 저장소에는 Urban Institute의 교육 데이터 API에 대한 액세스를 제공하는 MCP(Model Context Protocol) 서버가 포함되어 있습니다. 이 서버는 Claude와 함께 사용하여 교육 데이터에 쉽게 액세스할 수 있도록 설계되었습니다.
저장소 구조
education-data-package-r/: 교육 데이터 API에 접근하기 위한 원래 R 패키지(참조용)src/: MCP 서버 소스 코드build/: 컴파일된 MCP 서버
Related MCP server: Slate MCP Server
교육 데이터 API에 대하여
Urban Institute의 교육 데이터 API는 다음을 포함한 광범위한 교육 데이터에 대한 액세스를 제공합니다.
학교 및 학군 등록 데이터
대학 및 대학교 데이터
평가 데이터
재무 데이터
그리고 훨씬 더 많은 것들
API는 레벨(학교, 학군, 대학), 소스(CCD, IPEDS, CRDC 등), 주제(등록, 디렉토리, 재정 등)별로 구성됩니다.
특징
get_education_data도구를 통해 자세한 교육 데이터를 검색합니다.get_education_data_summary도구를 통해 집계된 교육 데이터를 검색합니다.리소스를 통해 사용 가능한 엔드포인트 찾아보기
설치
이 저장소를 복제하세요:
지엑스피1
종속성 설치:
npm install서버를 빌드하세요:
npm run buildnpx에서 서버를 사용할 수 있도록 설정하세요.
npm link
MCP 서버 구성
Claude와 함께 이 MCP 서버를 사용하려면 MCP 설정 구성 파일에 추가해야 합니다.
Claude 데스크톱 앱(macOS)용
~/Library/Application Support/Claude/claude_desktop_config.json 편집합니다.
{
"mcpServers": {
"edu-data": {
"command": "npx",
"args": ["edu-data-mcp-server"],
"disabled": false,
"alwaysAllow": []
}
}
}VSCode의 Claude를 위해
/home/codespace/.vscode-remote/data/User/globalStorage/rooveterinaryinc.roo-cline/settings/cline_mcp_settings.json 을 편집합니다.
{
"mcpServers": {
"edu-data": {
"command": "npx",
"args": ["edu-data-mcp-server"],
"disabled": false,
"alwaysAllow": []
}
}
}사용 가능한 도구
교육 데이터를 얻으세요
API에서 자세한 교육 데이터를 검색합니다.
매개변수:
level(필수): 쿼리할 API 데이터 레벨(예: '학교', '학군', '대학')source(필수): 쿼리할 API 데이터 소스(예: 'ccd', 'ipeds', 'crdc')topic(필수): 쿼리할 API 데이터 주제(예: '등록', '디렉토리')subtopic(선택 사항): 그룹화 매개변수 목록(예: ['인종', '성별'])filters(선택 사항): 쿼리 필터(예: {년도: 2008, 학년: [9,10,11,12]})add_labels(선택 사항): 해당되는 경우 변수 레이블을 추가합니다(기본값: false)limit(선택 사항): 결과 수를 제한합니다(기본값: 100)
예:
{
"level": "schools",
"source": "ccd",
"topic": "enrollment",
"subtopic": ["race", "sex"],
"filters": {
"year": 2008,
"grade": [9, 10, 11, 12]
},
"add_labels": true,
"limit": 50
}교육 데이터 요약 받기
API에서 집계된 교육 데이터를 검색합니다.
매개변수:
level(필수): 쿼리할 API 데이터 레벨source(필수): 쿼리할 API 데이터 소스topic(필수): 쿼리할 API 데이터 주제subtopic(선택 사항): 추가 매개변수(특정 엔드포인트에만 적용 가능)stat(필수): 계산할 요약 통계(예: 'sum', 'avg', 'count', 'median')var(필수): 요약할 변수(
by): 결과를 그룹화할 변수filters(선택 사항): 쿼리 필터
예:
{
"level": "schools",
"source": "ccd",
"topic": "enrollment",
"stat": "sum",
"var": "enrollment",
"by": ["fips"],
"filters": {
"fips": [6, 7, 8],
"year": [2004, 2005]
}
}사용 가능한 리소스
서버는 사용 가능한 엔드포인트를 탐색하기 위한 리소스를 제공합니다.
edu-data://endpoints/{level}/{source}/{topic}: 특정 교육 데이터 엔드포인트에 대한 정보
Claude를 사용한 예시 사용
MCP 서버가 구성되면 Claude와 함께 사용하여 교육 데이터에 액세스할 수 있습니다.
Can you show me the enrollment data for high schools in California for 2020?그러면 Claude는 MCP 서버를 사용하여 데이터를 검색하고 분석할 수 있습니다.
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
}개발
서버를 직접 실행하려면:
npm start개발 중에 서버를 감시 모드로 실행하려면:
npm run watch서버의 기능을 검사하려면:
npm run inspectornpx를 사용하여 서버를 실행하려면:
npx edu-data-mcp-server특허
MIT
Available Tools
2 toolsget_education_dataB
Retrieve education data from the Urban Institute's Education Data API
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| 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 | Default |
|---|---|---|---|
| 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 updates
- First observed
get_education_data - First observed
get_education_data_summary
TDQS
C2.9/5.0
Scored 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
Related MCP Connectors
Education Data MCP — US K-12 schools, districts, funding and child poverty.
Higher education data: tuition, graduation rates, and earnings
Access US federal award, recipient, agency, and spending analytics data from USAspending.gov.
College Scorecard MCP — US Department of Education College Scorecard API
Related MCP Servers
- FlicenseNot gradedqualityDmaintenanceConnects Technolutions Slate with Claude to query and analyze student enrollment demographics, diversity metrics, and enrollment funnel data through natural language.-
- FlicenseNot gradedqualityBmaintenanceConnects Claude to the Canvas LMS REST API, enabling natural language queries about courses, assignments, grades, deadlines, and announcements.1-
- AlicenseAqualityCmaintenanceEnables AI assistants to discover and explore university research, researchers, academic programs, and institutional data from OpenAlex and the U.S. College Scorecard, returning source-linked results without fabricating facts.9MIT