MCP Tools | Model Context Protocol Server Directory

The planetscale/mcp server bridges the gap between LLMs and your database schema, allowing AI agents to interact directly with PlanetScale databases. Instead of manually pasting table definitions or query results into a prompt, this tool enables the model to inspect schemas, execute read queries, and analyze data in real-time. For developers, this means faster debugging and the ability to generate accurate migrations or complex SQL queries based on the actual state of the database. It integrates seamlessly via the PlanetScale CLI, eliminating the need for custom middleware to expose your data layer to AI assistants. Compared to static context windows, this provides a dynamic, live interface to your production or development environments.

The Yuque-MCP-Server bridges the gap between LLMs and Yuque's structured knowledge bases. Instead of manually copying documentation into a prompt, this server allows AI models to programmatically query, read, and manage documents via the Yuque API. For developers, this means your AI agent can now perform real-time knowledge retrieval, update internal wikis, or analyze document trends directly within the chat interface. It effectively transforms Yuque from a passive storage site into an active context source for your AI workflow, eliminating the friction of context switching and manual data entry during technical documentation tasks.

The mcp-aiven server integrates Aiven's managed data infrastructure directly into your LLM workflow. Instead of toggling between your IDE and the Aiven console, this tool allows you to programmatically navigate projects and interact with managed instances of PostgreSQL, Apache Kafka, ClickHouse, and OpenSearch. For developers, this means the ability to query database states, verify service configurations, and manage cloud resources using natural language prompts within an MCP-compatible client. It effectively bridges the gap between high-level AI reasoning and low-level infrastructure management, reducing the cognitive load of context-switching during deployment or debugging phases.

The macrocosmos-mcp tool bridges the gap between static LLM knowledge and live social discourse by providing a standardized interface for X, Reddit, and YouTube data. Instead of building custom scrapers or managing multiple API authentications, developers can use this MCP server to pull real-time posts, user activity, and thread discussions directly into their model's context. It supports granular filtering by search phrases and date ranges, making it particularly useful for sentiment analysis, trend tracking, and competitive intelligence. By decoupling the data fetching logic from the application layer, it allows for seamless integration into any MCP-compliant client, transforming an LLM from a general reasoner into a real-time social monitor.

The supabase-mcp-server bridges the gap between LLMs and your Supabase backend by exposing database introspection and query capabilities via the Model Context Protocol. Instead of manually exporting schemas or copying table definitions into a chat window, this tool allows your AI assistant to directly explore your database structure and execute SQL queries in real-time. For developers, this means faster debugging and more accurate schema-aware code generation. It integrates seamlessly into any MCP-compliant host, turning your LLM into a functional database client that can verify data integrity or analyze table relationships without leaving the IDE.

The longportapp/openapi MCP server bridges the gap between LLMs and live financial markets. Instead of relying on static training data or generic search, this tool gives your AI agent direct programmatic access to real-time stock market data and execution capabilities. For developers building fintech applications or personalized trading bots, it eliminates the need to write custom API wrappers for market analysis. You can integrate it into any MCP-compatible client to enable workflows like automated portfolio monitoring, real-time price tracking, and AI-driven trade execution. It shifts the AI's role from a general financial advisor to an active operator with a live data feed.

The MindsDB MCP server transforms your LLM from a static chat interface into a dynamic data orchestrator. Instead of manually exporting CSVs or writing repetitive API glue code, developers can use this tool to create a unified interface across disparate databases and SaaS platforms. It effectively acts as an abstraction layer, allowing the model to query, analyze, and manipulate real-time data using a standardized protocol. This is particularly useful for building AI agents that need to perform complex cross-platform lookups or maintain state across multiple data sources without requiring a custom backend for every single integration.

The opentelemetry-mcp-server bridges the gap between LLMs and your observability stack by providing a standardized interface to OpenTelemetry-compliant backends. Instead of manually querying dashboards in Datadog, Grafana, or Dynatrace, developers can now use MCP-enabled clients to fetch traces and metrics directly within their AI workflow. This tool essentially turns your telemetry data into a context window, allowing an AI to analyze system performance, debug latency spikes, or correlate errors across distributed services in real-time. It eliminates the context-switching overhead by treating your observability backend as a queryable data source, making it ideal for automated root-cause analysis and performance auditing.

The agentinterviews_mcp tool bridges the gap between LLM orchestration and qualitative user research. Instead of manually scripting survey flows or analyzing transcripts in isolation, this MCP allows developers to programmatically trigger and manage AI-driven interviews directly from their IDE or AI agent. It transforms research from a static data collection process into a dynamic loop where you can deploy specialized interviewers, recruit participants, and pull raw qualitative insights back into your development context. For teams building user-centric products, this means integrating real-time user feedback loops directly into the technical workflow, replacing manual spreadsheets with a structured API-driven approach to qualitative data.

The nile-mcp-server provides a standardized interface for developers to interact directly with Nile's multi-tenant Postgres infrastructure via LLMs. Instead of manually switching between SQL clients and administrative dashboards, this MCP server allows your AI coding assistant to programmatically manage database schemas, query tenant-specific data, and handle user authentication flows. It bridges the gap between natural language intent and complex multi-tenant operations, making it ideal for building agentic workflows that need to perform administrative tasks or data inspections without human intervention. Unlike generic Postgres connectors, this is purpose-built for the Nile ecosystem, offering streamlined access to tenant and auth management that standard SQL drivers often lack. Integration is straightforward: once configured, your local or cloud-based LLM agent gains a secure, structured way to navigate your application's multi-tenant landscape.

The hn-server MCP tool provides a structured interface for interacting with Hacker News, transforming raw HTML from news.ycombinator.com into clean, machine-readable data. For developers building AI agents or automated research workflows, this tool eliminates the overhead of writing custom scrapers or managing complex DOM parsing. Instead of dealing with unstructured web content, you can query specific endpoints to retrieve categorized data across top stories, new submissions, 'Ask HN' threads, 'Show HN' projects, and job postings. It is particularly useful for integrating real-time tech trends and developer sentiment into LLM-driven applications. Unlike generic web search tools that return noisy snippets, this server offers high-signal, schema-consistent data designed specifically for programmatic consumption within an MCP-enabled environment.

This MCP tool provides a direct bridge between LLMs and Baserow, turning your no-code databases into actionable context. Instead of manually exporting CSVs or copying rows, developers can grant models the ability to query, filter, and manipulate structured data in real-time. The integration supports full CRUD operations—Create, Read, Update, and Delete—alongside powerful table search capabilities. For developers building agentic workflows, this means your AI can act as a sophisticated data administrator: it can look up customer records, update project statuses, or append new entries to a database based on conversation context. Unlike static data injections, this tool allows for dynamic, stateful interactions with your Baserow workspace, making it ideal for automating internal business logic or building intelligent CRUD interfaces via natural language.

For developers building location-intelligence tools or demographic analysis pipelines, mapping ZIP codes to counties is notoriously messy due to boundary overlaps. The ZIP-County Crosswalk MCP solves this by providing a direct interface to HUD’s official crosswalk datasets. Unlike simple lookup tables that might fail when a ZIP code spans multiple jurisdictions, this tool handles population-overlap filtering. This allows your agent or application to determine exactly which county holds the majority share of a specific ZIP code's population, ensuring higher data integrity for spatial queries. It integrates seamlessly via the Model Context Protocol, making it easy to plug into LLM-based workflows that require precise geographic context without manual data cleaning. Whether you are automating census-based reporting or refining logistics models, this tool provides a standardized, authoritative way to resolve spatial ambiguities.

Managing Percona PostgreSQL clusters in Kubernetes often involves a high cognitive load, juggling connection pooling via PgBouncer, Point-in-Time Recovery (PITR), and disaster recovery workflows. The mcp-percona-pg tool bridges the gap between LLM-driven orchestration and complex database operations. Instead of manually executing kubectl commands or navigating complex operator manifests, you can use this MCP server to delegate database tuning, backup management, and pool configuration directly through your AI coding assistant. It provides a standardized interface to interact with your Percona instances, making it particularly useful for SREs and backend engineers who want to automate routine maintenance or troubleshoot connectivity issues using natural language. Unlike generic database tools, this is purpose-built for the Percona ecosystem on K8s, ensuring that operations like scaling or recovery follow best practices by default.

The mcp-debezium MCP server bridges Model Context Protocol clients with Debezium and Kafka Connect, letting you monitor and manage Change Data Capture (CDC) connectors programmatically. It exposes connector lifecycle operations—status checks, configuration reads, restarts, and deletes—through a standardized MCP interface, so you can integrate CDC observability into agents, IDEs, or automation pipelines without writing custom Debezium API glue. The server supports access modes, connector allowlists, delete gating, and credential redaction to keep production environments safe while giving developers self-service control. Whether you're building a troubleshooting assistant, automating connector deployments, or embedding CDC insights into internal tools, mcp-debezium removes the friction of direct REST calls and lets you focus on higher-level logic. It's a community-maintained server, so expect a pragmatic fit for real-world Kafka Connect workflows rather than enterprise bells and whistles.

mcp-clickhouse is a Model Context Protocol server that connects ClickHouse to your MCP-compatible tools and agents. It lets you explore schemas, run SQL queries, and manage tables directly from an MCP client while enforcing safety controls: read/write/destructive classification, database allowlists, row caps, dry-run, and audit logging. Ideal for data analysts, ML engineers, and backend devs who want to query ClickHouse through AI assistants without exposing raw credentials or running risky statements. Integrates easily with any MCP host (Claude Desktop, custom agents) via standard MCP transport. Compared to ad-hoc DB clients, it adds structured governance for LLM-driven workflows—preventing accidental deletes, capping result sizes, and logging every action. Setup is straightforward: configure the server with your ClickHouse connection, define allowlists and limits, and start querying securely through your AI toolchain.

The mcp-kafka server bridges the gap between LLM-based reasoning and real-time data streaming infrastructure. For developers managing complex event-driven architectures, this tool moves beyond simple read-only queries. It provides a standardized interface for an AI agent to inspect cluster health, analyze consumer group lag, and manage topic lifecycles directly through a chat interface or automated workflow. Unlike standard CLI tools that require manual context switching, this MCP implementation integrates Kafka's operational metadata directly into your development environment. It includes critical production-grade safeguards such as topic allowlists, delete gating, and dry-run modes to prevent accidental data loss. Whether you are debugging pipeline bottlenecks or automating topic provisioning, this server turns your LLM into a context-aware Kafka operator that understands your specific cluster topology and access constraints.

Leadgen MCP provides programmatic access to Romanian business registry data from ONRC, the country's official registry. Developers can look up company details, search for directors by name or ID, extract website contact information, and perform domain audits including WHOIS, DNS, SPF, and DMARC records. It's designed for integration with AI agents and automation workflows that need reliable, structured business data from Romania. The tool acts as a bridge between raw registry data and practical applications, returning clean JSON responses. For comparison, it's more focused than general web scraping tools since it targets official sources directly, though it's geographically limited to Romanian entities. Use cases include sales prospecting, compliance verification, fraud detection, and building business intelligence dashboards. Integration follows standard MCP protocols, making it compatible with existing agent frameworks. You'll need the MCP client set up and can find implementation details on the GitHub repository linked in the metadata.