"Understanding and Building Probability Density Functions (PDF) Models" matching MCP servers:

MCP server to manage and deploy Scaleway Serverless Functions, enabling creation, listing, updating, deleting, downloading, and logging of functions through natural language.

A demonstration project for building and running Model Context Protocol (MCP) servers using Netlify Serverless Functions. It provides a template for deploying serverless MCP endpoints with automated CI/CD pipelines through Netlify.

An MCP server that enables AI assistants to discover and execute Nox sessions for project automation tasks like testing, linting, and building. It provides tools to list available sessions and run them using specific Python versions, tags, or keyword expressions.

Turns Claude Code into an interactive OpenWiFi triage assistant, providing tools for searching Linux wireless patches, Jira tickets, running LLM-based triage, backporting, and building OpenWiFi.

Enables AI assistants to set up, run, and manage Ballerina + Salesforce integrations inside a WSO2 Integrator workspace, providing 18 tools for OAuth2 token acquisition, project scaffolding, building, deploying, and stopping the integration service.

Enables building structured release evidence by comparing two Git refs and correlating commits, merged pull requests, linked issues, labels, changed files, and contributors.

Enables building MSBuild projects and solutions by dynamically locating MSBuild and providing customizable build options, with clear error reporting and cross-client compatibility.

MCP server for DCI API enabling AI models to extract and analyze DCI jobs, components, and topics, with integrations for Google Drive, Jira, GitHub, and Red Hat support cases.

A local MCP server that packages LLM evaluation gates as reusable CI/CD primitives, enabling AI agents to run datasets against models, score responses, and enforce quality thresholds.

Provides structured access to Xcode development tools including xcrun, xcodebuild, and xctrace for building, testing, and analyzing projects.

An MCP (Model Context Protocol) server that integrates with the ArgoCD API, enabling AI assistants and large language models to manage ArgoCD applications and resources through natural language interactions.

Enables AI agents to manage Databricks Lakeflow jobs by building and uploading Python wheels and triggering runs with specific arguments. It provides a structured way to orchestrate complex data experiments and monitor execution directly on Databricks clusters.

Enables building, maintaining, running, and reading reports for Apache JMeter test plans through typed MCP tools, without opening the GUI.

Enables building, verifying, previewing, deploying, diagnosing, updating, and rolling back full-stack Micro apps through typed local MCP tools backed by micro-cli.

Enables building and cleaning Delphi projects (.dproj/.groupproj) on Windows using MSBuild with RAD Studio environment initialization. Supports both individual projects and group projects with configurable build configurations and platforms.

The Model Context Protocol (MCP) Jenkins integration is an open-source implementation that bridges Jenkins with AI language models following Anthropic's MCP specification. This project enables secure, contextual AI interactions with Jenkins tools while maintaining data privacy and security.

A TypeScript starter template for building Model Context Protocol (MCP) servers with built-in support for tools, testing, and CI/CD pipelines. It provides a complete development environment for creating custom AI assistant integrations for tools like Claude, Copilot, and Cursor.

Enables interaction with Gerrit code review system via REST API, allowing language models to query changes, retrieve details, and manage reviews.

Enables orchestrating secure software development pipelines with domain-specific compliance (HIPAA, PCI-DSS, etc.), generating pseudocode, threat models, and CI/CD from user stories via natural language.

MCP server for GitLab that provides 85 tools covering issues, merge requests, repositories, CI/CD, wiki, packages, and attachments, with support for both gitlab.com and self-hosted instances. It can read attachments so models can see screenshots and other files.