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maintenanceEnables AI assistants to interact with Databricks workspaces, running SQL queries, managing jobs, and exploring schemas via the Model Context Protocol.
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"Tools and Templates for Prompt Management and Workflow Automation" matching MCP servers:
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- Flicense-qualityDmaintenanceAn MCP Server that provides a conversational interface to the UK Open Banking account information API, allowing agents to interact with bank account data through natural language commands.Last updated
- Flicense-qualityBmaintenanceMCP server for semantic search of 1C (BSL) code templates with 2262+ community templates, CRUD web interface, and ChromaDB + embeddings for meaning-based search.Last updated22
- FlicenseBqualityCmaintenanceRead-only MCP tools for TROCCO API, enabling workflow and BigQuery datamart audit information retrieval.Last updated2
- FlicenseCqualityDmaintenanceProvides automated Git workflow tools for committing, creating pull requests, and merging with proper GitHub authentication handling. Supports complete Git workflows from commit to merge with dry run capabilities and conventional commit formatting.Last updated41
- Alicense-qualityDmaintenanceEnables AI models to interactively prompt users for input or clarification directly through their code editor. It facilitates real-time communication between assistants and users during development tasks.Last updatedMIT
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- AlicenseAqualityCmaintenanceProvides structured spec-driven development workflow tools for AI-assisted software development with sequential spec creation (Requirements → Design → Tasks). Features a real-time web dashboard for monitoring project progress and managing development workflows.Last updated58894,231GPL 3.0
- AlicenseAqualityDmaintenanceAn MCP server that uses Claude 3.5 Sonnet to transform ordinary prompts into structured, professionally engineered instructions for any LLM. It enhances AI interactions by adding context, requirements, and structural clarity to raw user inputs.Last updated13MIT
- AlicenseAqualityBmaintenanceEnforces disciplined programming practices by requiring AI assistants to audit their work and produce verified outputs at each phase of development, following structured workflows for refactoring, feature development, and testing.Last updated203612MIT
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