Sold by: PingCAP
Deployed on AWS
Deploy a TiDB Cloud MCP server on Amazon Bedrock AgentCore Runtime and give AI agents MCP-based access to database discovery, SQL queries, SQL execution, and selected user management operations.
4.6
Overview
TiDB Cloud MCP Server brings live TiDB Cloud access into MCP-compatible agents and developer tools. It gives those clients a direct path to real schema and data, which is far more useful than asking an agent to reason from screenshots, copied DDL, or stale sample rows.
Delivered as a container image for Amazon Bedrock AgentCore Runtime, the server runs in your AWS environment and connects to the TiDB Cloud deployment you specify. You control the target cluster, the credentials, and the database privileges exposed to agents. That makes it possible to keep one deployment read-only for inspection workloads while reserving broader access for tightly controlled operational flows.
The current toolset includes show_databases, switch_database, show_tables, db_query, db_execute, db_create_user, and db_remove_user. Together, these tools support common workflows such as schema discovery, SQL-assisted debugging, application development against live TiDB Cloud data, and selected database administration tasks carried out through the MCP interface.
This product is intended for teams that already use TiDB Cloud and want to make that database context available to AI systems without building a custom MCP layer from scratch. Buyers must provide an existing TiDB Cloud deployment, valid database credentials, and network connectivity from the runtime environment to the target endpoint. If your TiDB Cloud environment uses IP allowlists, add the required addresses before testing.
Highlights
- Give AI agents live TiDB Cloud context instead of static schema notes, exported data, or manually prepared samples.
- Control which cluster, credentials, and database privileges are exposed to agents by running the MCP server in your own AWS environment.
- Support schema discovery, SQL-assisted investigation, application development, and selected database operations through a single MCP server deployment.
Details
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Delivery method
Type
Supported services
Delivery option
AgentCore Runtime deployment
Latest version
Operating system
Linux
Deployed on AWS
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Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
This product is available free of charge. Free subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Vendor refund policy
This is a free offering.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
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Delivery details
AgentCore Runtime deployment
Supported services: Learn more
- Amazon Bedrock AgentCore
Container image
Containers are lightweight, portable execution environments that wrap server application software in a filesystem that includes everything it needs to run. Container applications run on supported container runtimes and orchestration services, such as Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS). Both eliminate the need for you to install and operate your own container orchestration software by managing and scheduling containers on a scalable cluster of virtual machines.
Version release notes
Version 1 of TiDB MCP Server
Additional details
Usage instructions
Subscribe to the product and deploy the container on Amazon Bedrock AgentCore Runtime.
Provide the required environment variables:
TIDB_HOST
TIDB_PORT
TIDB_USERNAME
TIDB_PASSWORD
TIDB_DATABASE
The runtime environment must be able to reach your TiDB endpoint. If your TiDB Cloud deployment uses IP allowlists, add the required outbound addresses before testing.
After deployment, the server listens on port 8000 and exposes the MCP endpoint at POST /mcp.
Validate the deployment with MCP initialization and tools/list, then test one or more supported tools such as show_databases, show_tables, or db_query.
Use a database account with privileges appropriate for your use case. This server can expose write and administrative operations such as db_execute, db_create_user, and db_remove_user.
For deployment and configuration details, see https://docs.pingcap.com/ai/tidb-mcp-server/ . For more information about TiDB Cloud, see https://docs.pingcap.com/tidbcloud/ .
Resources
Vendor resources
Support
Vendor support
Please contact us at aws-marketplace@pingcap.com for more information.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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Customer reviews
Abhishek .
Seamless Integration, Reliable and Scalable
Reviewed on Feb 13, 2026
Review provided by G2
What do you like best about the product?
I really appreciate how flexible TiDB is. It allows our system to grow without needing big changes to the overall structure. Even as we add new features like more agents, more analytics, and more personalization, the database doesn't slow things down or become a limiting factor. Another thing that works really well is how TiDB stays out of the way. There's no need to constantly tweak settings or worry about scaling as usage changes. This kind of easy to maintain reliability is really useful when the main focus is on AI orchestration and user experience, not on managing the database itself. The initial setup was also smooth and simple to add to our current cloud system, which makes it fit well into the larger ecosystem without causing any extra work or complexity.
What do you dislike about the product?
Monitoring and performance tracking could be more user friendly. Even though the system is strong, having simpler, more focused insights would help new startups or hackathon teams learn faster.
What problems is the product solving and how is that benefiting you?
TiDB keeps user data and recommendations up-to-date and in sync, ensuring reliable nutrition advice. It handles structured health data safely, supports growth without slowing down, and is easy to maintain, letting us focus on AI orchestration and user experience.
Yuvraj s.
Reliable and Scalable Database Solution
Reviewed on Feb 13, 2026
Review provided by G2
What do you like best about the product?
I find TiDB incredibly reliable, which is crucial for ServiceBridge when dealing with real services and financial transactions through an in-app wallet. The trustworthiness of TiDB ensures that our records and processes stay intact without any system issues. I also appreciate how seamlessly it grows without needing a complete overhaul, allowing our team to focus on enhancing the user experience rather than dealing with technical challenges. Plus, setting up the database with TiDB Cloud was simple and integrated easily with our existing tools.
What do you dislike about the product?
There are areas that could be improved. While the core system is stable, getting a good grasp of how performance behaves in a distributed SQL setup can be tricky, especially for teams used to working with traditional single node databases. Also, monitoring and performance insights could be made easier for smaller teams. Having a more intuitive way to see how queries are behaving and how the system scales would make it easier to learn and use.
What problems is the product solving and how is that benefiting you?
I use TiDB for reliable transaction management, ensuring accurate data during money transfers and confirmations. It grows seamlessly, preventing redesigns and technical challenges, allowing us to focus on enhancing user experience. TiDB is trustworthy, especially for handling real services and financial operations.
Vamsi c.
Revolutionized Resume Matching with Seamless Database Integration
Reviewed on Feb 13, 2026
Review provided by G2
What do you like best about the product?
I like that TiDB has native support for vectors along with full compatibility with SQL. It allows us to seamlessly use semantic similarity search while managing structured candidate data and compliance processes. Its built-in vector support means we can store embeddings and conduct cosine similarity searches directly with relational data without needing to maintain separate systems. I also value TiDB's scalability and serverless approach, which helps us handle an increasing number of resume uploads and recruiter searches without infrastructure setup or maintenance.
What do you dislike about the product?
One area that could use improvement is the need for more guidance and examples that specifically focus on optimizing vector search at a large scale. Although TiDB's built-in vector search support functions well, adjusting the performance of similarity searches and choosing the right indexing methods required some trial and error during development. More hands-on documentation that's relevant to real-world applications, like resume matching or recommendation systems, would help teams learn and apply best practices more quickly.
What problems is the product solving and how is that benefiting you?
TiDB solves the challenge of merging semantic search with structured hiring by supporting both relational data and vector searches in one scalable solution. I like its scalability, serverless approach, and SQL compatibility, which help manage resume uploads and searches without extra infrastructure setup.
Sravan v.
Handles High Data Throughput and Analytics Seamlessly
Reviewed on Feb 12, 2026
Review provided by G2
What do you like best about the product?
I really appreciate how TiDB handles a lot of data coming in quickly and supports the execution of analytical queries effectively. It allows us to calculate volatility metrics and trigger alerts in real-time without managing separate systems. The scalability feature is great because as more users sign up and more trading pairs are monitored, the data increases rapidly, yet TiDB's distributed structure lets the system grow smoothly without the need for manual sharding or rewriting the database structure. Also, data consistency is a big positive, ensuring that price data, calculated values, and user alert settings are all in sync and dependable. The fact that it integrates well with MySQL made the integration smooth while still providing the advantage of a scalable distributed architecture.
What do you dislike about the product?
TiDB works well for our CryptoPulse workload, but understanding how to optimize distributed queries may need more learning than with a regular single node SQL database. Some queries had to be adjusted as the amount of data grew and having better performance tips for handling time series or high frequency data would be really useful.
What problems is the product solving and how is that benefiting you?
I use TiDB to handle high-frequency market data and analytics simultaneously, ensuring consistency and real-time calculations for cryptocurrency alerts. It scales smoothly with data growth and maintains data consistency, managing both transactional and analytical workloads without separate systems.
prasanth .
Crucial for Scalable, AI-Powered Emotional Context Processing
Reviewed on Feb 12, 2026
Review provided by G2
What do you like best about the product?
I really like TiDB Serverless for its built-in vector search and scalable setup. The cosine similarity search allows MindVector AI to match emotion and context vectors quickly without needing an extra database, making our design simpler. The serverless model automatically handles scaling, ensuring smooth performance during real-time recommendations. Its compatibility with MySQL made connecting to our Python backend very easy. Plus, TiDB's ability to manage both structured data and vectors in one platform increases efficiency, reduces operational complexity, and helps us develop AI-powered emotional support features more reliably.
What do you dislike about the product?
One part of TiDB Serverless that could use improvement is its documentation and examples, especially those related to vector search and AI applications. As a developer working on MindVector AI, I found it difficult at first to understand how to design a good vector schema, choose the right indexing methods, and fine-tuning performance because there weren't enough simple, practical examples. More tutorials that show how to integrate with machine learning pipelines, particularly reinforcement learning workflows, would be really helpful. Also, better debugging and visualization tools for checking vector similarity queries would make it easier for developers to check and trust their results. Adding more built-in support for AI-related tasks and clearer guidelines on best practices would definitely make development faster and greatly improve the overall experience for developers.
What problems is the product solving and how is that benefiting you?
I use TiDB for high-dimensional vector storage and real-time matching for personalized stress management. Its serverless scalability boosts performance, simplifying development with built-in vector search and single-system data management.