"A guide to managing GitLab projects" matching MCP servers:

Enables AI agents to programmatically manage projects, work items, modules, cycles, labels, views, and other Plane workspace data on 8seneca's Plane deployment.

MCP Server for the GitLab API, enabling project management, file operations, and more.

Enables AI assistants to query PostgreSQL, MySQL, SQL Server, Oracle, and MongoDB databases through MCP with read-only, audited access.

This project builds a read-only MCP server. For full read, write, update, delete, and action capabilities and a simplified setup, check out our free CData MCP Server for Zoho Projects (beta): https://www.cdata.com/download/download.aspx?sku=PZZK-V&type=beta

This MCP server lets AI agents connect to Figma to read, analyze, create, and modify designs, including elements, styles, assets, and code generation. It works with free Figma accounts and supports parallel multi-agent use across MCP-compatible clients.

Universal HTTP 402 Micropayment Protocol & Autonomous Economic Settlement Engine for AI Agents on Base L2.

Enables agents to transform tabular Markdown content into structured, type-inferred records with normalized formatting through an MCP-compatible interface.

Enables searching and retrieving content, module information, and Twig template snippets from a-blog cms via its GET API.

Enables converting Markdown and HTML tables into structured JSON records with automatic column type inference and format normalization. It also offers supporting web-agent primitives such as DOM semantic pruning, form field auto-mapping, and execution trajectory evaluation, all via a zero-dependency MCP server.

Lets an AI assistant read call and message history, send texts and picture messages, reach Phone Book contacts by name, and — once switched on — place calls run by a voice agent, all limited to the phone numbers the user ticks. Hosted remotely with OAuth sign-in, so nothing needs to be installed locally.

Enables agents to break high-level business objectives into dependency-ordered sub-task graphs and then directly generate the finished deliverables, such as financial spreadsheets, executive presentations, and analytical reports. It runs as a zero-dependency Python MCP server over JSON-RPC stdio, adding deterministic authorization, role-boundary validation, and telemetry.

MCP-first A/B testing server that enables agents to manage experiments (create, update traffic splits, read results, apply winning variants) from tools like Claude Code, with self-hosted Cloudflare backend.

Provides Figma design context to AI agents, enabling code generation, design system integration, and canvas manipulation through MCP.

Connects AI coding assistants to Anima Playground, Figma designs, and design systems, enabling handoff of designs to production code.

Thin MCP server that enables AI agents to generate video, image, and audio content via the Deepy API using a personal API key, with safe generation flow and no business logic.

Enables querying of a locally pinned snapshot of the Education-to-Workforce Indicator Framework — 99 indicators and 11.8M observations — to browse questions, describe metrics, resolve places, and fetch or rank data across national, state, county, and district levels.

Publishes what an AI made — a page, a markdown or PDF doc, or a whole site — to a live HTTPS link. Runs locally over stdio from the @drop2run/mcp npm package, or hosted at https://mcp.dropto.run/mcp for chats that connect by URL.

Exposes each stage of the Champollion MRI-to-sulcal-embedding pipeline as an MCP tool, plus a full-pipeline umbrella tool, letting agents launch stages without shelling out manually. Background jobs are tracked with status, logs, progress polling, cancellation, and preflight checks so runs can be monitored and debugged.

Enables agents to discover available still-image animation models, obtain credit quotes without charge, submit one explicitly approved generation job, and poll the resulting short clip.

What is PandaDoc MCP? PandaDoc MCP (Model Context Protocol) server connects AI agents directly to PandaDoc's document workflow — creating, sending, and tracking contracts and agreements without leaving your AI client. How to use PandaDoc MCP? To use PandaDoc MCP, connect your PandaDoc account, provide the required API credentials, and configure the appropriate MCP client.