Lists capability bundles grouping local MCP servers by tasks like software, filesystem, automation. Provides an overview of available server groups for easier selection and management.
Return the current server configuration and available browser backends, including the server name and backend versions, by querying the local filesystem as JSON.
Save a Jira attachment to the MCP server's filesystem using a cache key and target path. Enables server-side storage and processing of downloaded files.
Download Minecraft server instance files from MCSManager to the local filesystem for backup, analysis, or modification. Specify the remote file path and optional local output location.
Provides secure filesystem access for AI models through the Model Context Protocol with strict path validation, file operations, directory management, and system command execution within predefined directories.
Retrieve a Confluence attachment by ID as a base64-encoded resource, making file content available over MCP without server filesystem access. For files up to 50 MB.
Retrieve a Confluence attachment by ID as a base64-encoded resource for use over MCP, without server filesystem access. Attachments over 50 MB return an error.
Batch-scan MCP server names from fleet inventory to get per-server risk assessment with known CVEs and verdicts. Categorizes risk from filesystem, database, and search services.
Retrieve server details like version, implementation type, and available features for the URL Text Fetcher MCP Server that fetches web content and searches online.
Assess MCP server tool risks by live-introspecting servers, classifying capabilities like filesystem, network, code execution, and credential access to produce per-tool and per-server risk scores for safer trust decisions.
Run a workflow that checks MCP connectivity across Context7, GitHub, Filesystem, Database, and Browser servers. Diagnose integration failures to identify which MCP server is malfunctioning.
Generate a complete MCP server implementation from gathered requirements to automate project setup for AI-assisted development in TypeScript or Python.