GitHub API MCP Configuration
The GitHub API MCP configuration provides a hosted JSON schema that connects AI assistants — including Claude Desktop, Cursor, Windsurf, and VS Code Copilot — to the GitHub API API via the Model Context Protocol. This configuration maps 4 API endpoints as callable tools, including Get a repository, List repository issues, List pull requests, and more. It requires OAuth2 credentials to authenticate. The configuration is official-generated from the GitHub API OpenAPI specification (v2022-11-28) and has a quality score of 78/99 (good documentation coverage). Use the hosted URL below to auto-load this schema into any compatible MCP client.
Quick Specs Reference
Hosted Config URL
Use this hosted URL in any client that supports remote MCP schema auto-loading.
https://mcpbridge.org/config/github.jsonOne-Click Client Setup
Copy the configurations below to wire your local coding assistant directly.
Claude Desktop
claude_desktop_config.json{
"mcpServers": {
"github": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-github"
],
"env": {
"GITHUB_TOKEN": "your_github_personal_access_token"
}
}
}
}Cursor & VS Code
MCP Server URL Setup{
"mcpServers": {
"github": {
"url": "https://mcpbridge.org/config/github.json"
}
}
}Raw Configuration JSON
For local command line wrappers or dynamic shell bindings.
{
"mcpServers": {
"github": {
"command": "npx",
"args": ["-y","@modelcontextprotocol/server-github"],
"env": {
"GITHUB_TOKEN": "your_github_personal_access_token"
}
}
}
}Required Environment Keys
Substitute these secrets inside your configuration directory environment definitions.
GITHUB_TOKENyour_github_personal_access_token with your secret key credentialMapped Web APIs & Tools
The following routes will be exposed directly as protocol tools for the LLM.
/repos/{owner}/{repo}Get a repository
/repos/{owner}/{repo}/issuesList repository issues
/repos/{owner}/{repo}/pullsList pull requests
/repos/{owner}/{repo}/contents/{path}Get repository content
Similar Configurations
GitLab API
Manage repositories, CI/CD pipelines, and merge requests through your AI agent.
https://mcpbridge.org/config/gitlab.jsonBox Platform API
The Box Platform API, provided by Box (box.com), is a robust and comprehensive RESTful service that enables deep integration with the Box cloud content management ecosystem. It serves as the programmatic backbone for enterprises and developers seeking to build custom applications and workflows that interact with content stored securely in Box. Its core capabilities extend far beyond basic file operations, encompassing a full spectrum of content lifecycle management. Developers can programmatically create, upload, download, search, and manage files and folders, but the API's true power lies in its enterprise-grade features. These include advanced collaboration management through invitations and permissions, granular user and group administration within an enterprise directory, and sophisticated security and compliance controls. Specific endpoint groups for managing collaboration whitelists and exempt targets allow for precise governance over external sharing policies, ensuring that content is only shared with approved domains. Furthermore, the API facilitates complex legal and compliance use cases, such as placing items on legal hold or applying retention policies, making it an indispensable tool for regulated industries and large organizations. Exposing this API as tools via the Model Context Protocol (MCP) for AI coding assistants transforms it from a static integration point into a dynamic, conversational development partner. The value lies in delegating repetitive, structured, and context-aware platform operations to the AI agent. Instead of manually writing scripts or navigating multiple dashboard clicks, a developer can instruct the AI to perform precise actions using natural language, which the AI translates into the correct API calls. For instance, an AI assistant equipped with these MCP tools can intelligently query the `GET /collaborations` endpoint to analyze the permission landscape for a sensitive project folder, or it can generate the necessary configuration to programmatically whitelist a new partner domain using `POST /collaboration_whitelist_entries`. This drastically accelerates development and operational workflows, reduces the cognitive load on developers, and minimizes the risk of manual errors in scripting repetitive tasks, effectively embedding the Box Platform's capabilities directly into the developer's AI-augmented workflow. Within this MCP-enabled environment, a developer can instruct the AI agent to perform a variety of powerful, dynamic tasks. For example, a natural language command like, "Set up the standard folder structure for our new 'Project Phoenix' initiative under the Corporate Engineering directory, then add the legal team as collaborators with viewer-only permissions," can be orchestrated by the AI. It would sequentially create the folder hierarchy via the file management endpoints, search for the existing 'Legal' group using the user management APIs, and finally apply the correct permissions using the collaborations endpoint. Another practical workflow involves security auditing; a developer could ask, "List all external collaborations on files within the '2024 Financial Reports' folder and check if any are outside our approved vendor list." The AI agent would query the relevant endpoints, cross-reference the results against the collaboration whitelist entries via `GET /collaboration_whitelist_entries`, and provide a concise report or even take corrective action by revoking specific collaborations if instructed. Critical attention must be paid to authentication and security when implementing this API integration. While the described endpoints use a 'None' authentication method for the initial `GET /authorize` step (which is part of the OAuth 2.0 flow initiation), all subsequent data operations require a valid OAuth 2.0 access token. The principle of least privilege is paramount; developers must configure their applications with the narrowest OAuth scopes necessary for their specific use case, avoiding broad `read_write_all` scopes when `read_only` or scoped write access suffices. All tokens must be stored securely, and refresh tokens should be handled with care. For enterprise deployments, administrators should enable Box's IP whitelisting for API access and mandate two-factor authentication for associated accounts. Furthermore, developers must implement rigorous error handling and leverage Box's comprehensive webhook system for event-driven architectures, rather than relying solely on polling. Finally, all API interactions should be logged for audit trails, especially when managing compliance-related features like legal holds or retention policies, to ensure accountability and support for regulatory requirements.
https://mcpbridge.org/config/box-com.jsonAsana
This API serves as the programmatic backbone for the Asana work management platform, provided by Asana, Inc. It enables developers to interact programmatically with one of the world's leading enterprise collaboration and productivity suites. The core capabilities of this interface center around the CRUD (Create, Read, Update, Delete) operations for fundamental Asana objects. Specifically, the provided endpoints grant control over project attachments—allowing for the uploading, retrieval, and management of files associated with tasks and projects—and custom fields, which are pivotal for creating structured, data-rich workflows. These custom fields allow organizations to define unique data types (like dropdown menus, text fields, or dates) to standardize information capture across projects, moving beyond basic task lists to true operational tracking. Typical use cases span from enterprise project management offices (PMOs) needing to programmatically generate status reports and audit attachments, to development teams automating the creation of bug-tracking projects with predefined custom fields for severity and status, to operational leaders building dashboards that aggregate and analyze custom field data for resource allocation insights. When exposed as tools via the Model Context Protocol (MCP) to an AI coding assistant like Claude Desktop or Cursor, this API transforms from a static set of endpoints into a dynamic, conversational work orchestration layer. The value proposition is profound: it bridges the gap between natural language intent and structured work management execution. An AI assistant equipped with these MCP tools gains the ability to understand and manipulate the very fabric of a team's operational workflow. Instead of a developer manually writing scripts to query project attachments for an audit or updating custom fields to trigger a workflow state change, they can issue plain English commands. This integration enables the AI to act as a highly specialized "project operations agent," capable of reasoning about work data, making updates based on complex criteria, and automating routine administrative tasks that typically consume valuable engineering or management time. The context window allows the AI to maintain awareness of recent interactions, making iterative tasks like "find all attachments from last week and summarize them" or "change the 'Priority' field to 'High' for all tasks assigned to me due this week" seamless and efficient. Practical workflow examples highlight the powerful automation possibilities. A developer could instruct their AI agent: "Query all attachments on the 'Q3 Launch' project and generate a CSV list of filenames and their parent tasks for documentation." The AI would leverage the GET /attachments endpoint (with appropriate project filtering) to compile this report instantly. For a more complex update: "For every task in the 'Backlog' project that has the custom field 'Estimated Hours' set to more than 10, create a subtask titled 'Breakdown Required' and update the 'Status' custom field to 'Needs Refinement'." Here, the AI would orchestrate a sequence: first querying tasks using the custom fields API (once a GET for custom fields is available or via linked object data), then using the POST /batch endpoint to efficiently create multiple subtasks and update multiple custom fields in a single, optimized API call. Furthermore, an agent could be tasked with "Set up a new bug report template by creating a 'Bug' project and adding the custom fields 'Bug ID' (text), 'Severity' (dropdown), and 'Component' (dropdown) with the appropriate options," automating a multi-step project setup process that would otherwise require numerous manual clicks or complex scripting. Despite the current configuration indicating no authentication requirement for this specific API definition, a rigorous approach to security is non-negotiable in any real-world implementation. Developers must treat this API as a conduit to their organization's critical work data. All interaction must be authenticated using Asana's standard OAuth 2.0 flow or Personal Access Tokens, ensuring every action is attributable and authorized. The principle of least privilege is essential: create and use API tokens with the narrowest possible scope. For instance, if a tool's sole purpose is to read attachments, its token should not have permission to delete them or modify project structures. When deploying an MCP server, it is critical to securely manage and store credentials, avoiding hardcoding and utilizing environment variables or secret management services. Network security should enforce HTTPS for all API calls, and developers should implement robust error handling and logging to monitor for unusual activity without exposing sensitive data. Rate limiting awareness is also key to building resilient applications that respect Asana's API service limits.
https://mcpbridge.org/config/asana-com.jsonclickup20
The clickup20 Polls API is a lightweight, focused web service designed to facilitate the creation and management of simple polling mechanisms. Provided by ClickUp, a platform known for its project management and productivity tools, this API serves as a specialized component for gathering quick, quantitative feedback. Its core capabilities are straightforward: it allows consumers to programmatically retrieve a list of existing poll questions and to submit new poll questions for consideration. Typical use cases span both enterprise and consumer domains. In an enterprise setting, a development team might integrate this API to run quick polls during sprint retrospectives, gauge internal sentiment on a new tool, or gather binary feedback on proposed technical designs within a project management workflow. For consumer applications, it could power simple feedback widgets within a mobile app or website, enabling users to vote on feature priorities or content topics. The API’s simplicity, requiring no authentication, makes it highly accessible for rapid prototyping and integration into internal tools where complex credential management is unnecessary. Exposing this API as a tool via the Model Context Protocol (MCP) to an AI coding assistant like Claude Desktop, Cursor, or Cline unlocks significant value by transforming static data into a dynamic, interactive resource. The primary value lies in enabling the AI agent to become an active participant in the polling lifecycle, rather than a passive observer. With MCP, the AI can understand the API’s schema and endpoints as callable functions. This allows the assistant to proactively fetch the current state of polls to inform its responses or code suggestions, or to initiate changes based on developer commands. For instance, an AI could be instructed to analyze the results of a poll about preferred coding frameworks and then use that context to suggest project boilerplate code or library imports. This bridges the gap between raw data and actionable intelligence, making the AI a more context-aware and integrated development partner that can manipulate external data sources as part of its reasoning process. Practical workflow examples demonstrate how a developer can leverage this MCP server for dynamic task automation. A developer could instruct the AI agent, “Query all current poll questions and summarize the topics to ensure we haven’t duplicated efforts.” The AI would then use the GET /questions tool to retrieve the data, analyze it, and provide a concise summary. Another powerful workflow involves automating data entry: “Based on the discussion in our last stand-up notes file, create three poll questions to help the team decide on our next tech debt priority.” The AI would parse the notes, formulate appropriate questions, and use the POST /questions tool to create them, significantly reducing manual overhead. Furthermore, an agent could be tasked with maintaining a historical record, such as, “Every Monday at 9 AM, fetch the list of poll questions and append them to a running log in our team’s documentation repository,” creating an automated audit trail without continuous human intervention. Given that the clickup20 API currently operates with no authentication method, developers must exercise extreme caution and implement stringent security best practices at the integration and network layers. While the absence of authentication simplifies setup, it inherently exposes the API to unauthorized access and data manipulation. The principle of least privilege is critical; this API should only be exposed on highly secure, private networks or within isolated development environments. Public exposure must be avoided entirely. Implementing a proxy or gateway that adds an authentication layer (such as API keys, OAuth 2.0, or JWT verification) before forwarding requests to the API is a mandatory mitigation. Developers must also ensure that sensitive data is not included in poll questions and that the API’s functionality is not relied upon for any critical or proprietary decision-making processes. Configuration should involve strict firewall rules and thorough review of any AI agent’s actions to prevent unintended data modification or exfiltration.
https://mcpbridge.org/config/clickup-com.json