"GitHub Copilot" matching MCP servers:

Bridges Microsoft Foundry agents and Power BI semantic models into GitHub Copilot Chat, enabling users to query and modify measures as the signed-in user via MCP.

Enables AI agents to query Microsoft Copilot Studio telemetry and governance data in plain English, pulling from multiple Azure and M365 APIs into a local SQLite database.

Local-first Taiwan market evidence workbench for AI agents with persistent Watchlists, governed MCP, auditable evidence, and explicit execute-once retrieval. GitHub Repository URL

Enables management of GitHub workflows, issue templates, and repository configuration for the Frontal MCP Server project through CI/CD pipelines and security automation.

A read-only MCP server and CLI that enables AI agents and users to read Google Search Console and GA4 metrics across multiple Google accounts, plus WordPress.org, Firefox Add-ons, and GitHub stats, and generate weekly snapshots.

Enables any MCP client to run a complete data-science pipeline on a CSV: exploration, cleaning, model comparison, training, tuning, and plain-language analysis. Exposes nine tools that turn raw data into trained models and reports.

Enables authorized DatumLabMHQ GitHub members to access the Datum data platform through MCP tools such as whoami, list_products, list_questions, ask, query, and health, without holding API keys.

A production-ready MCP server that provides comprehensive dbt project quality assessment for any GitHub repository, enabling AI agents to analyze dbt models, check metadata coverage, and map data lineage.

MCP server for the OrionBelt Semantic Layer. Enables LLMs to explore governed semantic models, compile YAML metric definitions into optimized SQL across 8 engines (BigQuery, ClickHouse, Databricks, Dremio, DuckDB, MySQL, PostgreSQL, Snowflake), and execute analytics queries via natural language. Works with Claude, Cursor, Windsurf, and Copilot.

Provides a secure, policy-driven read-only gateway that exposes multiple databases to AI tools via MCP, with governed select, search, and aggregate operations and no free-form SQL.

Enables AI agents and MCP clients to convert ASCII/OCR text dumps, aligned spacing, and CSV/TSV data into clean GitHub Flavored Markdown tables and typed JSON deterministically.

Enables users to submit, log, fetch, and analyze advocacy and community-program activities across Google GDE, Microsoft MVP, YouTube, Dev.to, Hashnode, GitHub, AWS, and other programs, with OAuth-secured and encrypted credential storage.

Enables language models to safely explore lakehouse tables and governed metrics, run guarded read-only SQL, and inspect pipeline health and data-contract violations through MCP tools.

Exposes a synthetic issue tracker and pipeline warehouse as callable tools so an agent can answer operational questions about tickets, pipeline health, runs, incidents, and governed metrics with every claim cited to the exact tool call it came from. All writes are proposal-only, requiring human approval through a gated apply path that logs each step for audit.

A unified backend MCP server that aggregates and filters data from external services like Zaim, GitHub, Google, and Notion, providing cross-cutting views via MCP and REST API without heavy AI inference.

An MCP server that gives AI assistants the ability to connect to, query, profile, and monitor data sources — turning any LLM into an interactive data engineering copilot.

A conversational copilot that guides users through the Knowledge Discovery in Databases (KDD) process to perform automated data profiling, analysis, and modeling via natural language. It enables the creation of interactive dashboards and manages the end-to-end analytical workflow within a secure sandbox environment.

Exposes nine governed analytics tools — attrition, headcount, tenure, salary, promotion, and more — plus a restricted read-only SQL execution path, so an HR analyst's plain-English question resolves to a versioned, reproducible method instead of free-form LLM-written SQL. Tool selection is driven by the LLM, while role-based access control, PII screening, SQL guards, result validation, and audit logging keep every answer within policy.

Enables exposing any Databricks capability as MCP tools for AI agents, allowing MCP clients like Claude, AI Playground, and Copilot Studio to discover and call them without client-side changes.

An MCP server that orchestrates deterministic analytics, benchmarks, recommendations, and forecasting to provide AI-driven campaign analysis and executive reports.