Top 10 Best MCP (Model Context Protocol) Servers in 2025

In 2025, the rapid rise of AI-driven applications, agent-based workflows, and large language models has pushed Model Context Protocol (MCP) servers to the forefront of modern architecture.

MCP servers allow AI assistants and applications to securely interact with data, APIs, and services through a standardized protocol, powering everything from document search to infrastructure automation.

Choosing the right MCP server impacts productivity, scalability, integration potential, and compliance, making expert-reviewed server picks and specifications crucial for modern deployments.

This guide presents the top 10 MCP servers of 2025, ranked for innovation, features, integration, and operational benefit, catering to developers, enterprise teams, and AI platform architects alike.

Why MCP (Model Context Protocol) Servers 2025

MCP servers form the backbone of contextual interaction between advanced AI systems and external resources.

With ecosystems expanding across cloud, security, finance, DBMS, and DevOps, these servers offer seamless connections, standardized query/reporting, and dynamic context retrieval for LLMs and agents.

Key factors driving the adoption of MCP servers in 2025 include:

The need for robust, secure, and extensible agent frameworks across cloud and API-centric architectures.

Growing requirements for compliance, auditability, and traceability in AI systems.

Demand for real-time, semantic access to knowledge, operations, and workflow automations.

Accelerating integration of AI-driven tooling within enterprise production environments.

Standardization and open APIs for scaling knowledge retrieval across distributed teams and projects.

Competitive edge in leveraging AI for data-driven insights and infrastructure automation.

Comparison Table: Top 10 MCP (Model Context Protocol) Servers 2025

Tools	Vector DB	Code Scan	Financial Data	DBMS	API	DevOps	Docs	Semantic Search
Chroma	✅ Yes	❌ No	❌ No	✅ Yes	✅ Yes	❌ No	❌ No	✅ Yes
GreptimeDB	❌ No	❌ No	❌ No	✅ Yes	✅ Yes	❌ No	❌ No	✅ Yes
Semgrep	❌ No	✅ Yes	❌ No	❌ No	✅ Yes	❌ No	❌ No	✅ Yes
Octagon	❌ No	❌ No	✅ Yes	✅ Yes	✅ Yes	❌ No	❌ No	✅ Yes
Financial Datasets	❌ No	❌ No	✅ Yes	❌ No	✅ Yes	❌ No	❌ No	✅ Yes
ClickHouse	❌ No	❌ No	❌ No	✅ Yes	✅ Yes	❌ No	❌ No	✅ Yes
Appwrite	❌ No	❌ No	❌ No	✅ Yes	✅ Yes	❌ No	✅ Yes	✅ Yes
JFrog	❌ No	❌ No	❌ No	❌ No	✅ Yes	✅ Yes	❌ No	✅ Yes
Gmail	❌ No	❌ No	❌ No	❌ No	✅ Yes	❌ No	❌ No	✅ Yes
Terraform	❌ No	❌ No	❌ No	❌ No	✅ Yes	✅ Yes	✅ Yes	✅ Yes

1. Chroma

Why We Picked It

Chroma MCP Server sets the standard for semantic document management and retrieval in AI-driven workflows, leveraging vector search and database capabilities.

Its ability to scale from development to production, combined with strong open-source roots and persistent storage options, makes it a favorite among teams seeking advanced LLM contextual memory.

The server stands out for its seamless integration with AI assistants and major IDEs, fostering productivity and cross-platform compatibility.

With comprehensive collection management APIs, Chroma MCP Server excels in context enrichment and recall for applications and R&D.

Its architecture supports both ephemeral and persistent storage, enabling flexibility for scalable deployments.

Chroma is widely adopted in academic, enterprise, and dev tools spaces due to its fast search, rich feature set, and support for modern query methods.

Specifications

Chroma MCP Server is compatible with Python 3.8+, supports both ephemeral and persistent client types, and offers HTTP and cloud deployment options.

Its robust APIs facilitate CRUD operations and support advanced document, metadata, and semantic search capabilities for large datasets.

Features

Key features include semantic and full-text search, metadata/context filtering, persistent storage, comprehensive error handling, bidirectional links, and support for automated codebase indexing and test-driven learning.

Reason to Buy

Developers, data scientists, and enterprises can use Chroma to build knowledge hubs, semantic search engines, and integrated context recall systems for smarter AI agents.

Its plug-and-play integrations with Claude Desktop, Cursor, and other MCP clients offer a strong ecosystem advantage.

Pros

• Powerful semantic search
• Persistent and ephemeral storage options
• Open-source and community-supported
• Rich error handling and validation
• Supports major AI platforms

Cons

• Setup may require Python familiarity
• Occasional compatibility gaps with legacy data formats

✅ Best For: Semantic document management and AI-driven context enrichment

🔗 Try Chroma here → "Chroma Official Website"

2. GreptimeDB

Why We Picked It

GreptimeDB MCP Server is engineered for secure, real-time database interaction through AI agents, making it ideal for metrics, logging, and time-series analytics.

It enables teams to expose structured database operations to AI, unlock contextual queries, and integrate with Claude Desktop or Cursor for controlled database access.

The server excels at providing a safe, structured layer for SQL execution, table listing, and data analysis, all inside MCP-supported IDEs and clients.

Its focus on real-time analytics makes it a strong candidate for observability, monitoring, and large-scale analytical tasks.

GreptimeDB’s MCP server is under active development, driving innovation in data privacy and secure access for AI agents.

Its support for credential-based authentication and configuration ensures safe multi-user deployments.

Specifications

Supports deployment via Python, integrates directly with GreptimeDB’s cloud or local database, and provides customizable authentication and session configuration.

Integration through environment variables or command-line arguments allows granular control over connection and session management.

Features

Secure SQL execution, table/resource listing, prompt management, database schema exploration, and controlled read/write operations within the database environment.

Reason to Buy

Perfect for organizations needing structured, agent-driven access to time-series databases or real-time analytics pipelines. Delivers productivity via controlled, AI-assisted database exploration and analysis.

Pros

• Real-time metrics and analytics
• Secure integration for AI assistants
• Extensive command and query support
• Low-latency and scalable performance
• Flexible deployment options

Cons

• Security/privacy best practices need improvement
• Still in experimental/beta development for some features

✅ Best For: Observability, monitoring, time-series analytics

🔗 Try GreptimeDB here → "GreptimeDB Official Website"

3. Semgrep

Why We Picked It

Semgrep MCP Server merges AI support with deterministic static code analysis, empowering dev teams to scan codebases for vulnerabilities and compliance issues using custom rules.

Open-source and language-agnostic, it’s embraced by security specialists for its fast scan speeds and rich rule library.

Integration with major IDEs and MCP agents transforms static analysis into a conversational, context-aware workflow, significantly reducing false positives and improving code health.

Advanced features like custom rule creation, semantic issue highlighting, and scan result export help organizations automate quality assurance.

Continuous updates and strong community engagement make Semgrep MCP Server a forward-looking choice for secure software development.

Specifications

Supports CLI, Docker, and MCP protocol, enabling scans on local directories/files or remote projects. Compatible with all major programming languages used in modern enterprises.

Features

Code scanning, custom rule management, semantic analysis, scan export to JSON/SARIF/text, differential scan reporting, and strong language coverage.

Reason to Buy

Security analysts and developers gain a context-driven, fast, and highly customizable vulnerability detection solution. Easily leverages AI assistants to automate compliance and code reviews.

Pros

• Broad language support
• Free and open source
• Advanced semantic scanning
• Rich rule set for compliance
• Fast and reliable scan results

Cons

• Requires setup for custom rules
• Initial integration may need attention

✅ Best For: Automated code security audits and compliance

🔗 Try Semgrep here → "Semgrep Official Website"

4. mcp-octagonofficial

Why We Picked It

Octagon MCP Server offers deep financial data access powered by specialized AI agents for public filings, transcripts, market data, and private company profiles.

Its orchestration across multiple market intelligence sources establishes it as the go-to for financial research, investment analysis, and deep corporate insights.

Integration with Claude Desktop and Cursor amplifies business intelligence and enables conversational deep-dive into finance.

Octagon stands out for real-time research agility and massive coverage in both public and private markets, supporting advanced use cases like M&A analytics and VC funding.

Specifications

Structured for major AI agents (Claude, Cursor), supports API orchestration, and integrates with Octagon Market Intelligence for scalable research.

Credentials-based secure access and dedicated endpoints for each specialization.

Features

SEC filings analysis, transcript parsing, financial metric/ration analytics, stock data access, private market screenings, deep web-based research, and multi-source data aggregation.

Reason to Buy

Financial analysts, investors, and market researchers benefit from aggregated insights, unique market intelligence, and powerful MCP/AI agent orchestration.

Pros

• Comprehensive market intelligence
• Multi-source data aggregation
• Real-time and historical data analysis
• Strong AI agent integration
• Free access for most features

Cons

• Requires API key for advanced features
• Some modules are specialized and may need setup

✅ Best For: Investment research, market intelligence, financial analytics

🔗 Try mcp-octagonofficial here → "Octagon Official Website"

5. Financial Datasets

Why We Picked It

The Financial Datasets MCP Server stands out for developers and data-driven teams needing millisecond latency access to stock market APIs, income statements, balance sheets, cashflow data, and real-time market news.

Designed for secure, authenticated AI assistant access, it supports workflows in trading, investment research, and financial compliance.

Its seamless integration gives MCP agents reliable, up-to-date information for any company or cryptocurrency.

Straightforward setup, REST and SSE interfaces, and strong support for data retrieval are major benefits.

Specifications

Supports Python 3.10+, uv package manager, encrypted API setup, and multi-client connectivity (Claude Desktop, Cursor, Windsurf). Provides granular tools for financial statements, price data, and news.

Features

Income statement/balance sheet/cashflow retrieval, stock and crypto price monitoring, company and news lookup, historical data analysis, and automated compliance reporting.

Reason to Buy

Ideal for fintechs, trading bots, and financial news agents. Delivers critical datasets on-demand with robust security and low latency.

Pros

• Ultra-fast real-time financial API
• Rich toolset for data analysis
• Secure, authenticated access
• Crypto support included
• Easy integration for AI agents

Cons

• Requires API key for usage
• Advanced querying may need customization

✅ Best For: Financial data APIs, trading bots, investment platforms

🔗 Try Financial Datasets here → "Financial Datasets Official Website"

6. ClickHouse

Why We Picked It

ClickHouse MCP Server excels in agentic analytics, scalable OLAP, and real-time reporting, connecting ClickHouse DBMS to AI-powered agents.

Its advanced SQL toolset, database introspection, and health check APIs make ClickHouse a leader in high-performance analytics and enterprise-grade observability.

The server supports both local and cloud setups, integrating seamlessly into production analytics dashboards and MCP-enabled environments.

Strong compatibility with CopilotKit and cloud providers ensures wide adoption in data engineering.

Specifications

Supports Python, Docker, and cloud-native deployment. Offers HTTP/SSE interfaces for remote agent connection, health monitoring, and SQL command execution.

Features

Comprehensive SQL query execution, DB/table/model discovery, health monitoring, and security-driven read-only query configuration.

Reason to Buy

Analysts and engineers love ClickHouse for its speed, scalability, and agentic operational integration in MCP-centric analytics workflows.

Pros

• High-performance OLAP queries
• Scalable to petabytes of data
• DB introspection and analytics
• Secure and compliant
• Multi-platform/agent ecosystem

Cons

• Advanced config may require DB expertise
• Some features gated by enterprise edition

✅ Best For: High-performance analytics, agent-based reporting

🔗 Try ClickHouse here → "ClickHouse Official Website"

7. Appwrite

Why We Picked It

Appwrite MCP Server bridges backend app development and MCP-powered AI agents, offering documentation, API, and SDK context for code generation, troubleshooting, and best practice guidance.

It vastly improves the productivity of backend teams, helping automate user management, database actions, security audits, and feature implementations via natural language interfaces.

With semantic documentation search, code sample retrieval, and robust authentication, Appwrite MCP Server is perfect for high-velocity developer workflows.

Specifications

Supports Node.js, API key authentication, and multi-client access through Claude Desktop, VS Code, and Cursor.

Provides both API and documentation endpoints and seamless integration for LLM/code assistants.

Features

Semantic doc search, code sample retrieval, best practices guidance, intelligent troubleshooting, backend API automation, secure access to project resources.

Reason to Buy

Backend engineers and RAD teams save time and reduce errors by leveraging AI-assisted coding, API calls, and context-rich development support.

Pros

• Documentation and API support
• Real-time context for code generation
• Best practices guidance
• Seamless integration with major IDEs
• Secure user, database, and function management

Cons

• Needs initial setup for integration
• Some features API-key gated

✅ Best For: Fast backend app development, MCP-powered coding agents

🔗 Try Appwrite here → "Appwrite Official Website"

8. JFrog

Why We Picked It

JFrog MCP Server brings DevOps and secure supply chain management into the AI/agent conversation space, enabling integration with Artifactory, CI/CD pipelines, and security scanning tools.

Its remote SaaS model requires no local installs, keeping MCP agents updated and accessible for all JFrog cloud accounts.

JFrog MCP Server is indispensable for release automation, artifact curation, and real-time vulnerability status polling.

Its fast setup and secure API means easier workflow optimization for developers, DevOps, and SecOps.

Specifications

Compatible with all JFrog cloud accounts, supports remote (SaaS) deployment, and integrates seamlessly with MCP clients in IDEs, code assistants, and desktop AI agents.

Features

DevOps pipeline management, release lifecycle automation, artifact repository access, CI/CD triggering, vulnerability scans, structured API output for AI queries.

Reason to Buy

DevOps teams gain effortless automation, release security checks, and agile supply chain curation, all accessible through AI-powered agents.

Pros

• End-to-end supply chain visibility
• Automated security and release checks
• Fast SaaS onboarding
• Secure API for MCP agents
• Always up to date

Cons

• Requires JFrog Cloud subscription
• Beta features subject to change

✅ Best For: DevOps automation, secure supply chain, artifact management

🔗 Try JFrog here → "JFrog Official Website"

9. Gmail

Why We Picked It

Gmail MCP Server enables frictionless, secure management of Gmail accounts via natural language, integrating AI agents for sending, receiving, searching, and organizing email.

Advanced attachment support, OAuth2 authentication, and batch operations make Gmail MCP Server the top choice for teams needing AI-assisted communication, workflow automation, and inbox organization.

Its broad support for Claude Desktop, Cursor, and custom endpoints ensures easy onboarding and versatile productivity.

Specifications

Implements OAuth2, supports desktop and web credentials, offers full integration with Gmail API, and provides batch and MIME operations. Compatible with major MCP clients and direct API.

Features

Send/receive emails, read by ID, HTML/multipart support, attachment management, label processing, batch actions, secure authentication, comprehensive search.

Reason to Buy

Productivity-focused teams benefit from AI-powered email automation, advanced search, and seamless integration into agent-driven task flows.

Pros

• Full Gmail integration
• Attachment and label support
• Secure authentication (OAuth2)
• Batch and advanced search
• Cross-platform agent compatibility

Cons

• Some destructive operations are disabled
• Initial setup requires Google API credentials

✅ Best For: AI-driven email management, workflow automation

🔗 Try Gmail here → "Gmail MCP Official Website"

10. Terraform-Cloud

Why We Picked It

Terraform-Cloud MCP Server pioneers AI-driven Infrastructure as Code automation, allowing teams and agents to manage, provision, and monitor cloud infrastructure using natural language.

Its features include access to real-time provider docs, registry integration, workspace and run management, and compliance automation.

Ideal for DevOps, cloud architects, and enterprises seeking to boost productivity, security, and auditability.

Powerful registry and Sentinel policy integration ensures up-to-date, compliant infrastructure deployments and rapid CI/CD adoption.

Specifications

Runs on Docker or prebuilt binaries, supports both local and remote deployment, and offers extensive API connectivity for provider/module lookup, state/version management, and cost estimation.

Features

Provider documentation access, registry API integration, module/search retrieval, workspace/run/project management, cost estimation, and audit-compliant logging.

Reason to Buy

Perfect for teams needing scalable, secure IaC automation, compliance reporting, and streamlined infrastructure provisioning via MCP agents.

Pros

• Real-time documentation access
• Registry and module integration
• Security and audit-compliant outputs
• Docker/container ready
• Easy integration with AI agents

Cons

• Beta status for some features
• Requires Docker or compatible platform

✅ Best For: AI-driven infrastructure management, cloud automation

🔗 Try Terraform-Cloud here → "Terraform Official Website"

Conclusion

The rapid evolution of Model Context Protocol servers in 2025 is reshaping how AI agents interact with data, services, and automation pipelines.

These top 10 MCP servers were selected for their innovation, reliability, extensibility, and productivity benefits.

Whether enhancing data search, code security, financial analysis, infrastructure automation, or everyday productivity, MCP servers are the new backbone of agentic digital workflows.

Selecting the right server from this list ensures secure, scalable, and future-ready AI deployments across enterprise, research, and developer spaces.