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Education Data MCP Server

by ckz
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JavaScript
Remote

教育数据MCP服务器

此存储库包含一个 MCP（模型上下文协议）服务器，用于访问城市研究所的教育数据 API。该服务器旨在与 Claude 配合使用，以便轻松访问教育数据。

存储库结构

  • education-data-package-r/ ：用于访问教育数据 API 的原始 R 包（仅供参考）

  • src/ ：MCP 服务器源代码

  • build/ ：已编译的 MCP 服务器

Related MCP server: Slate MCP Server

关于教育数据 API

城市研究所的教育数据 API可以访问广泛的教育数据，包括：

  • 学校和学区入学数据

  • 学院和大学数据

  • 评估数据

  • 财务数据

  • 以及更多

API 按级别（学校、学区、学院）、来源（ccd、ipeds、crdc 等）和主题（招生、目录、财务等）进行组织。

特征

  • 通过get_education_data工具检索详细的教育数据

  • 通过get_education_data_summary工具检索汇总的教育数据

  • 通过资源浏览可用端点

安装

  1. 克隆此存储库：

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

  2. 安装依赖项：

    npm install

  3. 构建服务器：

    npm run build

  4. 使服务器可用于 npx：

    npm link

配置 MCP 服务器

要将此 MCP 服务器与 Claude 一起使用，您需要将其添加到您的 MCP 设置配置文件中。

适用于 Claude 桌面应用程序 (macOS)

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

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

VSCode 中的 Claude

编辑/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": []
    }
  }
}

可用工具

获取教育数据

从 API 中检索详细的教育数据。

参数：

  • level （必需）：要查询的 API 数据级别（例如，“学校”、“学区”、“大学”）

  • source （必需）：要查询的 API 数据源（例如，'ccd'、'ipeds'、'crdc'）

  • topic （必需）：要查询的 API 数据主题（例如，“注册”、“目录”）

  • subtopic （可选）：分组参数列表（例如，['race'，'sex']）

  • filters （可选）：查询过滤器（例如，{year: 2008，grade: [9,10,11,12]}）

  • add_labels （可选）：适用时添加变量标签（默认值：false）

  • limit （可选）：限制结果数量（默认值：100）

例子：

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

获取教育数据摘要

从 API 中检索汇总的教育数据。

参数：

  • level （必需）：要查询的 API 数据级别

  • source （必需）：要查询的 API 数据源

  • topic （必填）：需要查询的API数据主题

  • subtopic （可选）：附加参数（仅适用于某些端点）

  • stat （必需）：要计算的汇总统计数据（例如，“总和”、“平均值”、“计数”、“中位数”）

  • var （必需）：需要汇总的变量

  • by （必填）：对结果进行分组的变量

  • filters （可选）：查询过滤器

例子：

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

可用资源

服务器提供浏览可用端点的资源：

  • edu-data://endpoints/{level}/{source}/{topic} ：有关特定教育数据端点的信息

Claude 的示例用法

一旦 MCP 服务器配置完成，您就可以使用它与 Claude 访问教育数据：

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

然后，Claude 可以使用 MCP 服务器来检索和分析数据：

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
}

发展

直接运行服务器：

npm start

要在开发期间以监视模式运行服务器：

npm run watch

检查服务器的功能：

npm run inspector

要使用 npx 运行服务器：

npx edu-data-mcp-server

执照

麻省理工学院

Available Tools

2 tools
get_education_dataB

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

ParametersJSON Schema
NameRequiredDescriptionDefault
levelYesAPI data level to query (e.g., 'schools', 'school-districts', 'college-university')
sourceYesAPI data source to query (e.g., 'ccd', 'ipeds', 'crdc')
topicYesAPI data topic to query (e.g., 'enrollment', 'directory')
subtopicNoOptional list of grouping parameters (e.g., ['race', 'sex'])
filtersNoOptional query filters (e.g., {year: 2008, grade: [9,10,11,12]})
add_labelsNoAdd variable labels when applicable (default: false)
limitNoLimit 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
NameRequiredDescriptionDefault
levelYesAPI data level to query
sourceYesAPI data source to query
topicYesAPI data topic to query
subtopicNoOptional additional parameters (only applicable to certain endpoints)
statYesSummary statistic to calculate (e.g., 'sum', 'avg', 'count', 'median')
varYesVariable to be summarized
byYesVariables to group results by
filtersNoOptional 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.

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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

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