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OSSInsight MCP Server

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OSSInsight MCP Server

An MCP server based on OSSInsight.io, providing data analysis for GitHub individuals, organizations, and repositories, as well as in-depth insights into the open source ecosystem.

Features

  • Repository Analysis: Get comprehensive data about GitHub repositories, including star trends, contributor activity, and code commits

  • Developer Analysis: Understand developers' contribution patterns, activity history, and influence

  • Organization Analysis: View a global perspective of GitHub organizations, including members, repositories, and overall activity

  • Project Comparison: Compare key metrics between two repositories side by side to discover differences and similarities

  • Project Collections: Browse and explore curated collections of projects, such as open-source databases, AI tools, etc.

  • Natural Language Queries: Access OSSInsight's chat interface to ask questions about GitHub data

Related MCP server: GitHub MCP Server

Tools

  1. get_repo_analysis

    • Get detailed analysis of a GitHub repository

    • Input:

      • owner_repo (string): Repository name in the format 'owner/repo'

      • time_period (optional string): Time range for analysis

    • Returns: Repository analysis data from both API and web page, with a link to the OSSInsight page

  2. get_developer_analysis

    • Get detailed analysis of a GitHub developer

    • Input:

      • username (string): GitHub username

    • Returns: Developer data from both API and web page, with a link to the OSSInsight page

  3. get_collection

    • Get information about a specific collection of repositories

    • Input:

      • collection_id (string): Collection ID, e.g., 'open-source-database'

    • Returns: Collection data and a link to the collection page on OSSInsight

  4. list_collections

    • List all available repository collections

    • Input:

      • page (optional number): Page number, starting from 1

      • per_page (optional number): Number of results per page, default is 20

    • Returns: List of collections and a link to browse collections on OSSInsight

  5. natural_language_query

    • Direct access to OSSInsight's natural language query interface

    • Input:

      • query (string): Natural language query, e.g., 'Which repositories gained the most stars in 2023?'

    • Returns: A direct link to OSSInsight's chat interface with the query prefilled

Implementation Details

This MCP server uses a dual approach to retrieve data:

  1. OSSInsight Public API (v1) - Makes direct API calls to https://api.ossinsight.io/v1/ endpoints

  2. Web Page Scraping - Falls back to scraping the OSSInsight web pages when APIs are limited or unavailable

This approach ensures maximum coverage of OSSInsight's features while respecting the public API's rate limits (600 requests per hour per IP).

Setup

Usage Configuration

Docker

{
  "mcpServers": { 
    "ossinsight": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "mcp/ossinsight"
      ]
    }
  }
}

NPX

{
  "mcpServers": {
    "ossinsight": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-ossinsight"
      ]
    }
  }
}

Build

Docker build:

docker build -t mcp/ossinsight -f Dockerfile .

Examples

Get Repository Analysis

// Get analysis data for the VSCode repository
{
  "owner_repo": "microsoft/vscode"
}

Compare Two Repositories

// Compare React and Vue
{
  "repo1": "facebook/react",
  "repo2": "vuejs/vue"
}

Natural Language Query

// Get a link to query data using natural language
{
  "query": "Which database projects gained the most stars in 2023?"
}

License

This MCP server is licensed under the MIT License. This means you can freely use, modify, and distribute this software, subject to the terms and conditions of the MIT License. See the LICENSE file in the project repository for details.

OSSInsight MCP 服务器

基于 OSSInsight.io 的 MCP 服务器，提供对 GitHub 个人、组织和仓库的数据分析，以及开源生态系统的深入洞察。

功能特点

  • 仓库分析：获取有关 GitHub 仓库的全面数据，包括星标趋势、贡献者活动和代码提交

  • 开发者分析：了解开发者的贡献模式、活动历史和影响力

  • 组织分析：查看 GitHub 组织的全局视图，包括成员、仓库和整体活动

  • 项目比较：并排比较两个仓库的关键指标，发现差异和相似之处

  • 项目集合：浏览和探索精选的项目集合，如开源数据库、AI 工具等

  • 自然语言查询：访问 OSSInsight 的聊天界面，用自然语言提问 GitHub 数据

工具

  1. get_repo_analysis

    • 获取 GitHub 仓库的详细分析

    • 输入:

      • owner_repo (字符串): 仓库名称，格式为 'owner/repo'

      • time_period (可选字符串): 分析的时间范围

    • 返回: 来自 API 和网页的仓库分析数据，以及指向 OSSInsight 页面的链接

  2. get_developer_analysis

    • 获取 GitHub 开发者的详细分析

    • 输入:

      • username (字符串): GitHub 用户名

    • 返回: 来自 API 和网页的开发者数据，以及指向 OSSInsight 页面的链接

  3. get_collection

    • 获取特定集合的信息和仓库列表

    • 输入:

      • collection_id (字符串): 集合 ID，例如 'open-source-database'

    • 返回: 集合数据以及指向 OSSInsight 集合页面的链接

  4. list_collections

    • 列出所有可用的仓库集合

    • 输入:

      • page (可选数字): 页码，从 1 开始

      • per_page (可选数字): 每页结果数量，默认为 20

    • 返回: 集合列表以及指向 OSSInsight 浏览集合页面的链接

  5. natural_language_query

    • 直接访问 OSSInsight 的自然语言查询界面

    • 输入:

      • query (字符串): 自然语言查询，例如 'Which repositories gained the most stars in 2023?'

    • 返回: 指向 OSSInsight 聊天界面的直接链接，并预填充查询

实现细节

此 MCP 服务器使用双重方法获取数据：

  1. OSSInsight 公共 API (v1) - 直接调用 https://api.ossinsight.io/v1/ 端点

  2. 网页抓取 - 当 API 受限或不可用时，回退到抓取 OSSInsight 网页

这种方法确保了对 OSSInsight 功能的最大覆盖，同时尊重公共 API 的速率限制（每个 IP 每小时 600 个请求）。

设置

使用配置

Docker

{
  "mcpServers": { 
    "ossinsight": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "mcp/ossinsight"
      ]
    }
  }
}

NPX

{
  "mcpServers": {
    "ossinsight": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-ossinsight"
      ]
    }
  }
}

构建

Docker 构建:

docker build -t mcp/ossinsight -f src/ossinsight/Dockerfile .

示例用法

获取仓库分析

// 获取 VSCode 仓库的分析数据
{
  "owner_repo": "microsoft/vscode"
}

比较两个仓库

// 比较 React 和 Vue
{
  "repo1": "facebook/react",
  "repo2": "vuejs/vue"
}

自然语言查询

// 获取使用自然语言查询数据的链接
{
  "query": "哪些数据库项目在 2023 年获得了最多的星标？"
}

许可证

此 MCP 服务器基于 MIT 许可证。这意味着您可以自由使用、修改和分发此软件，但需遵守 MIT 许可证的条款和条件。详情请参阅项目存储库中的 LICENSE 文件。

Available Tools

5 tools
get_collectionC

Get information about a specific collection of repositories

ParametersJSON Schema
NameRequiredDescriptionDefault
collection_idYesCollection ID, e.g., 'open-source-database'

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 this is a read operation ('Get information'), which is helpful, but doesn't describe what information is returned (e.g., metadata, repository list, permissions), error conditions, or any rate limits or authentication requirements.

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, clear sentence with no wasted words. It's appropriately sized for a simple lookup tool and front-loads the core purpose immediately.

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 no annotations and no output schema, the description is incomplete. It doesn't explain what information is returned about the collection (e.g., name, description, repositories, permissions), which is critical for an agent to understand the tool's utility. The lack of behavioral context and output details leaves significant gaps.

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?

Schema description coverage is 100%, with the single parameter 'collection_id' documented in the schema. The description doesn't add any additional meaning about the parameter beyond what the schema provides (e.g., examples of valid IDs beyond 'open-source-database', format constraints). Baseline 3 is appropriate when the schema does the heavy lifting.

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 ('Get information about') and resource ('a specific collection of repositories'), making the purpose understandable. However, it doesn't distinguish this tool from its sibling 'list_collections', which presumably lists multiple collections rather than retrieving details about a specific one.

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. It doesn't mention the sibling 'list_collections' for browsing collections or 'get_repo_analysis' for repository-level details, nor does it specify prerequisites like needing a collection ID first.

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

get_developer_analysisC

Get detailed analysis of a GitHub developer, including their activity and contributions.

ParametersJSON Schema
NameRequiredDescriptionDefault
usernameYesGitHub username

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 full burden for behavioral disclosure. It mentions 'detailed analysis' but doesn't specify what that entails—e.g., whether it includes metrics like commit frequency, pull requests, or language usage. It also lacks information on rate limits, authentication needs, or response format, which are critical for a tool with no output schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. It could be slightly more structured by explicitly listing analysis components, but it avoids redundancy and stays focused, earning a high score for conciseness.

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?

Given the complexity of developer analysis and the absence of both annotations and an output schema, the description is incomplete. It doesn't explain what 'detailed analysis' includes, how results are returned, or any behavioral traits like data freshness or limitations. For a tool with no structured output documentation, this leaves significant gaps for an AI agent.

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 input schema has 100% description coverage, with the 'username' parameter clearly documented as 'GitHub username'. The description adds no additional parameter details beyond what the schema provides, such as format constraints or examples. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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 tool's purpose: 'Get detailed analysis of a GitHub developer, including their activity and contributions.' It specifies the verb ('Get'), resource ('GitHub developer'), and scope ('detailed analysis'). However, it doesn't explicitly differentiate from sibling tools like 'get_repo_analysis' or 'natural_language_query', which prevents a perfect score.

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. It doesn't mention when to choose this over 'get_repo_analysis' for repository-level insights or 'natural_language_query' for broader queries. There's no context about prerequisites or exclusions, leaving usage decisions ambiguous.

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

get_repo_analysisC

Get detailed analysis of a GitHub repository, including activity, stars, issues, and other metrics.

ParametersJSON Schema
NameRequiredDescriptionDefault
owner_repoYesRepository name in the format 'owner/repo'
time_periodNoTime range for analysis (optional)

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. While it mentions what metrics are included, it doesn't describe important behavioral aspects like whether this is a read-only operation, what permissions might be required, whether there are rate limits, what format the analysis returns, or if there are any side effects. The description is too minimal for a tool that presumably makes API calls to GitHub.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately concise with a single sentence that efficiently communicates the core purpose. It's front-loaded with the main action ('Get detailed analysis') and includes relevant details. There's no wasted verbiage or unnecessary elaboration, though it could benefit from slightly more context.

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 that presumably makes external API calls to GitHub and returns complex analysis data, the description is incomplete. With no annotations and no output schema, the description should provide more context about what 'detailed analysis' includes, what format it returns, any authentication requirements, rate limits, or error conditions. The current description leaves too many important questions unanswered for effective tool use.

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 description adds no specific parameter information beyond what's already in the schema. With 100% schema description coverage, both parameters are well-documented in the schema itself. The description doesn't provide additional context about parameter usage, constraints, or examples that would help an agent understand how to use them effectively.

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 tool's purpose with specific verbs ('Get detailed analysis') and resources ('GitHub repository'), and lists key metrics included (activity, stars, issues). It distinguishes itself from sibling tools like 'get_collection' or 'list_collections' by focusing on repository analysis rather than collections. However, it doesn't explicitly differentiate from 'get_developer_analysis', which might be a related sibling.

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. There's no mention of when this analysis tool is appropriate versus other sibling tools like 'get_developer_analysis' or 'natural_language_query'. It lacks any context about prerequisites, limitations, or typical use cases for repository analysis.

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

list_collectionsB

List all available repository collections

ParametersJSON Schema
NameRequiredDescriptionDefault
pageNoPage number, starting from 1
per_pageNoNumber of results per page, default is 20

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. While 'List all available repository collections' implies a read-only operation, it doesn't mention pagination behavior (implied by parameters), rate limits, authentication requirements, or what format the results will be in. For a tool with no annotation coverage, this is insufficient.

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 with zero wasted words. It's appropriately sized for a simple list operation and front-loads the essential information.

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?

For a simple list tool with 2 documented parameters and no output schema, the description is minimally adequate but has clear gaps. It doesn't explain the return format, pagination behavior, or how this tool differs from siblings. Without annotations, the description should provide more behavioral context about what 'listing' entails.

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?

Schema description coverage is 100%, so the schema already fully documents both parameters. The description doesn't add any parameter semantics beyond what's in the schema - it doesn't explain how pagination works, default values, or constraints. Baseline 3 is appropriate when the schema does all the parameter documentation work.

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 ('List') and resource ('repository collections'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its sibling 'get_collection' which presumably retrieves a specific collection rather than listing all available ones.

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 like 'get_collection' or 'natural_language_query'. There's no mention of prerequisites, appropriate contexts, or limitations that would help an agent choose between available options.

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

natural_language_queryC

Query GitHub data using natural language through the OSSInsight chat interface

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language query, e.g., 'Which repositories gained the most stars in 2023?'

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool queries data but doesn't describe what happens during execution—e.g., whether it's read-only, requires authentication, has rate limits, or returns structured data. For a query tool with zero annotation coverage, this leaves significant gaps in understanding its behavior and constraints.

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 function without unnecessary words. It is front-loaded with the core purpose and uses clear terminology. Every part of the sentence earns its place by specifying key details like the data source and interface.

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?

Given the complexity of a natural language query tool with no annotations and no output schema, the description is incomplete. It doesn't explain what kind of results to expect, how queries are processed, or any limitations. For a tool that interacts with an external interface (OSSInsight chat), more context on behavior and output is needed for effective use.

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?

Schema description coverage is 100%, with the single parameter 'query' fully documented in the schema as a natural language string. The description adds no additional parameter semantics beyond what the schema provides, such as query formatting tips or examples beyond the schema's example. Baseline 3 is appropriate since the schema does the heavy lifting.

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 tool's purpose: 'Query GitHub data using natural language through the OSSInsight chat interface.' It specifies the action (query), resource (GitHub data), and method (natural language via OSSInsight). However, it doesn't explicitly differentiate from sibling tools like get_developer_analysis or get_repo_analysis, which might also query GitHub data but with different approaches.

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. It mentions the OSSInsight chat interface but doesn't explain when natural language queries are preferred over structured queries or how this differs from sibling tools like get_collection or list_collections. There are no explicit when/when-not statements or named alternatives.

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.

  1. 5 tool updates
    • First observedget_collection
    • First observedget_developer_analysis
    • First observedget_repo_analysis
    • First observedlist_collections
    • First observednatural_language_query
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TDQS

A3.5/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap. get_collection and list_collections handle collections, get_developer_analysis focuses on developers, get_repo_analysis targets repositories, and natural_language_query provides a unique chat interface. The descriptions make it easy to differentiate between these five distinct functions.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with snake_case naming. The verbs (get, list, natural_language) are appropriately descriptive for their actions, and the nouns (collection, developer_analysis, repo_analysis, query) clearly indicate the target resources. There are no deviations in naming conventions.

Tool Count5/5

With 5 tools, this server is well-scoped for its purpose of providing GitHub data analysis through OSSInsight. Each tool serves a specific, valuable function without redundancy, covering collections, repositories, developers, and natural language queries. This count is appropriate and manageable for the domain.

Completeness4/5

The tool surface covers the core OSSInsight functionality well, including collection management, repository and developer analysis, and natural language queries. A minor gap exists in the lack of update or delete operations for collections, but given the server's focus on data retrieval and analysis rather than management, this is a reasonable limitation that agents can work around.

Maintenance

ActivityInactive
ResponsivenessSyncing

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