Past battle · 2026-05-26 UTC
MCP Servers Showdown — May 26, 2026
From the MCP Servers category. 99 marks placed across 5 fighters. onchain-mcp took the crown.
Final standings
The line-up
The fighters
Profiles of every tool that competed in this battle, ranked by their final score.
The Bankless Onchain MCP Server is a framework for interacting with on-chain data via the Bankless API. It implements the Model Context Protocol (MCP) to allow AI models to access blockchain state and event data in a structured way. The server provides various data operations, including contract state reading, event logs fetching, and transaction history retrieval. It caters to developers and researchers who need to interact with blockchain data in a structured manner. This project is no longer receiving updates, and its maintenance status might affect its stability and feature availability.
- Contract operations (read contract state, get proxy, get ABI, get source)
- Event operations (get events, build event topic)
- Transaction operations (get transaction history, get transaction info)
MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines. It is most comparable to textract, but with a focus on preserving important document structure and content as Markdown, including headings, lists, tables, links, etc. The output is often reasonably presentable and human-friendly, but it is meant to be consumed by text analysis tools, and may not be the best option for high-fidelity document conversions for human consumption. MarkItDown currently supports the conversion from PDF, PowerPoint, Word, Excel, Images (EXIF metadata and OCR), Audio (EXIF metadata and speech transcription), HTML, Text-based formats (CSV, JSON, XML), ZIP files, Youtube URLs, EPubs, and more. It is recommended to use a virtual environment to avoid dependency conflicts. With Python 3.10 or higher, you can install MarkItDown using pip: pip install 'markitdown[all]' or from the source with: git clone git@github.com:microsoft/markitdown.git, then pip install -e 'packages/markitdown[all]'. The usage of MarkItDown involves command-line invocation, either by specifying the output file, piping content, or using the narrowest convert_* function for specific use cases.
- Conversion of PDF, PowerPoint, Word, Excel
- Support for Images (EXIF metadata and OCR)
- Support for Audio (EXIF metadata and speech transcription)
- Support for HTML, Text-based formats (CSV, JSON, XML)
- Support for ZIP files, Youtube URLs, EPubs
- Optional dependencies for activating various file formats
The mcp-clickhouse MCP server is an An MCP server for ClickHouse. It features ClickHouse Tools, including run_query to execute SQL queries on your ClickHouse cluster, list_databases to list all databases on your ClickHouse cluster, and list_tables to list tables in a database with pagination. Additionally, it includes chDB Tools, like run_chdb_select_query, to execute SQL queries using chDB's embedded ClickHouse engine. It also provides a Health Check Endpoint to check the server's health and a Security mechanism for authentication. The server can be set up for internal services, local development, or with OAuth / OIDC authentication providers through FastMCP.
- run_query to execute SQL queries on ClickHouse cluster
- list_databases to list all databases on ClickHouse cluster
- list_tables to list tables in a database with pagination
- run_chdb_select_query to execute SQL queries using chDB's embedded ClickHouse engine
- Health Check Endpoint for server monitoring
- Multiple authentication modes, including OAuth and OIDC through FastMCP
MCP Server for QA Sphere TMS is a client used for integrating Large Language Models (LLMs) with QA Sphere (QSP) to boost test script creation capabilities. After configuring the server (details available on GitHub), LLMs can interact with QA Sphere's automated test cases. By leveraging the MCP (Model Callback Protocol), it enables developers and testers to quickly create AI-based test cases, automate tasks, and run test suites integrated with QAS Sphere. The MCP-based solution is user-backed, allowing a wide range of QA tasks to be automated, including test-case discovery and execution. Additionally, you can reference large language models, automate tasks, and run test suites integrated with the QA Sphere Test Management System.
MemoryMesh
A knowledge graph server that uses the Model Context Protocol (MCP) to provide structured memory persistence for AI models. v0.2.8
MemoryMesh is a knowledge graph server designed for AI models, with a focus on text-based RPGs and interactive storytelling. It helps AI maintain consistent, structured memory across conversations, enabling richer and more dynamic interactions. The project is based on the Knowledge Graph Memory Server from the MCP servers repository and retains its core functionality. MemoryMesh empowers users to build and manage structured information for AI models. MemoryMesh is a local knowledge graph server that is particularly well-suited for text-based RPGs but can also be used for social network simulations, organizational planning, or any scenario involving structured data. Key Features Dynamic Schema-Based Tools: Define your data structure with schemas, and MemoryMesh automatically generates tools for adding, updating, and deleting data. Intuitive Schema Design: Create schemas that guide the AI in generating and connecting nodes, using required fields, enumerated types, and relationship definitions. Metadata for AI Guidance: Use metadata to provide context and structure, helping the AI understand the meaning and relationships within your data. Relationship Handling: Define relationships within your schemas to encourage the AI to create connections between related data points. Informative Feedback: Provides error feedback to the AI, enabling it to learn from mistakes and improve its interactions with the knowledge graph. Event Support: An event system tracks operations, providing insights into how the knowledge graph is being modified. Nodes represent entities or concepts within the knowledge graph, each having a name, nodeType, metadata, and weight.Edges represent relationships between nodes, each having a source node, target node, and edgeType. Schemas are the heart of MemoryMesh and define the data structure with automatically generated tools.
- Dynamic schema-based tools
- Intuitive schema design
- Metadata for AI guidance
- Relationship handling
- Informative feedback
- Event support