--- title: vinkius-mcp-registry canonical_url: "https://www.modelscope.ai/datasets/renatomarinho/vinkius-mcp-registry" md_url: "https://www.modelscope.ai/datasets/renatomarinho/vinkius-mcp-registry.md" repository: renatomarinho/vinkius-mcp-registry last_updated: 2026-09-28 license: cc0-1.0 storage_size: "16 MB" downloads: 1626 stars: 5 --- # vinkius-mcp-registry > vinkius-mcp-registry - An open-source dataset by renatomarinho on ModelScope. Vinkius Connector Registry — Open Data Initiative renatomarinho/vinkius-mcp-registry is a dataset on ModelScope. totalling 16 MB. licensed under cc0-1.0. - **Repository**: renatomarinho/vinkius-mcp-registry - **License**: cc0-1.0 - **Storage size**: 16 MB - **Downloads**: 1626 - **Stars**: 5 - **Last updated**: 2026-09-28 Source: https://www.modelscope.ai/datasets/renatomarinho/vinkius-mcp-registry --- # Vinkius Connector Registry — Open Data Initiative Welcome to the **Vinkius Open Data Initiative**. We are opening access to the Vinkius connector catalog. This repository provides automatically updated documentation for **10,270 unique connectors for AI agents**. ## Research & Training Applications This highly structured corpus is designed specifically for AI researchers, data scientists, and language model developers. It provides a robust foundation for advancing artificial intelligence research in the following domains: - **LLM Fine-Tuning**: Real-world schemas for training models in advanced function calling and tool utilization. - **Agentic Frameworks**: Operational metadata for studying multi-agent orchestration and system bridging. - **Semantic Analysis**: A large-scale dataset for analyzing how external software platforms and enterprise systems are mapped to natural language interfaces. ## Data Provenance Extracted programmatically from the Vinkius registry, each record maintains strict structural integrity. The dataset is rigorously indexed with categorical taxonomies, precise tool support configurations, and environmental metadata to ensure a noise-free corpus. --- ## Dataset at a Glance | Metric | Value | |---|---| | Total MCP servers | **10,270+** | | Refresh cadence | Every **12 hours** | | File format | `vinkius_mcp_servers.csv` (UTF-8) | | License | CC0 1.0 — Public Domain | | Source | [vinkius.com](https://vinkius.com) | --- ## Schema Reference | Column | Type | Description | |---|---|---| | `slug` | string | Unique server identifier | | `title` | string | Server display name | | `category` | string | Primary classification (e.g., Development, Data Analytics, Communication) | | `tags` | string | Comma-separated descriptive keywords | | `short_description` | string | One-line capability summary | | `description` | string | Full capability and integration details | | `tools_count` | integer | Number of tools exposed by this server | | `tool_names` | string | Comma-separated list of tool names | | `prompt_examples` | string | Real-world usage prompts designed for this server | | `debugger_grade` | string | Automated quality grade (A+ through F) | | `debugger_score` | float | Numeric reliability score assigned by the Vinkius Debugger | | `created_at` | datetime | Listing creation timestamp | | `url` | string | Direct link to the MCP server page on the Vinkius catalog (format: https://vinkius.com/en/ai-agent-connect/{slug}) | --- ## Research & Use Cases This dataset enables a range of applications across AI research, product development, and market intelligence: ### Ecosystem Intelligence - Map the growth trajectory of MCP server categories over time. - Identify which tool types (database, API, file system, communication) are expanding fastest. ### Prompt Engineering - Analyze `prompt_examples` to study how tool-use instructions are designed for different LLM integrations. - Build prompt template corpora for fine-tuning or evaluation benchmarks. ### Quality & Reliability Analysis - Explore the distribution of `debugger_grade` and `debugger_score` to identify patterns in high-quality vs. poorly maintained servers. - Correlate `tools_count` with reliability scores to measure complexity vs. stability trade-offs. ### Machine Learning - **Multi-label classification** — Predict `category` or `tags` from `description` using NLP models. - **Recommendation systems** — Build tool recommenders based on tag similarity, embedding distance, or collaborative filtering. - **Anomaly detection** — Flag servers with abnormal score patterns or sudden grade changes across snapshots. ### Market & Competitive Research - Track the rate of new server registrations as a proxy for MCP ecosystem adoption. - Benchmark server categories against industry verticals for strategic planning. --- ## Update Frequency This dataset is **automatically synchronized every 12 hours** from the Vinkius platform. Each snapshot reflects the most current listings, tool inventories, debugger scores, and quality grades available. | Property | Detail | |---|---| | Sync interval | Every 12 hours | | Pipeline | Automated | | Deduplication | Guaranteed unique slugs per snapshot | | Data integrity | Minimum threshold validation before each upload | --- ## Citation If you use this dataset in your research or product, we appreciate a citation: ```bibtex @dataset{vinkius_mcp_registry, title = {Vinkius Connector Registry — Global AI Agent Connector Dataset}, author = {Vinkius}, year = {2026}, url = {https://www.modelscope.ai/datasets/renatomarinho/vinkius-mcp-registry}, note = {Updated every 12 hours. 10,270+ connectors indexed.} } ``` --- ## About Vinkius [Vinkius](https://vinkius.com) is the connectivity layer and connector catalog for AI agents. With **10,270+ connectors** cataloged, graded, and deployable in one click, Vinkius is the definitive catalog for discovering and connecting AI agents — Claude, Cursor, GPT, Copilot, and any MCP-compatible client — to the tools they need. Beyond the catalog, Vinkius delivers a full infrastructure stack: **MCP Governance** (DLP, policies, compliance and audit), **Sandboxes** (isolated V8 execution for AI agents), **Analytics** (observability, cost and performance), and an **MCP Inspector** that runs 34 automated checks to grade every server from A+ to F. The platform is backed by open source projects including [MCP Fusion](https://github.com/vinkius-labs/mcp-fusion) (the MCP server framework for TypeScript), [MCP Desktop](https://github.com/vinkius-labs/mcp-desktop), [Discover MCP](https://github.com/vinkius-labs/discover-mcp), and [Doc-Breach](https://github.com/vinkius-labs/doc-breach-mcp). Trusted by engineers from Google, NVIDIA, Amazon, JPMorgan Chase, Red Hat, and more — Vinkius is where the MCP ecosystem is cataloged, tested, and connected. --- ## Independent Platform Disclaimer Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by any third-party company listed in this dataset. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use in this dataset is strictly for informational purposes to identify service compatibility and interoperability. --- ## License This dataset is released under the **CC0 1.0 Universal (Public Domain)** license. You are free to use, modify, and distribute it for any purpose — commercial or non-commercial — without attribution requirements, though citation is appreciated.