awesome-mcp-servers
Centralizes discovery and integration of MCP servers, reducing engineering time spent evaluating fragmented tooling options and accelerating deployment of st…
TensorBlock's community-curated directory of MCP servers — currently indexing 7,747 unique server links across category docs — served as a hosted, searchable registry with a free public API and install-config previews. A discovery resource for finding MCP servers, not an MCP server you install.
- Find MCP servers that integrate with your existing development tools and workflows
- Review available MCP server implementations to understand protocol capabilities and patterns
- Discover specialized MCP servers for specific coding tasks or technology stacks
Centralizes discovery and integration of MCP servers, reducing engineering time spent evaluating fragmented tooling options and accelerating deployment of standardized AI-assisted development workflows across teams.
Development teams standardizing on MCP-based tooling and platform engineers building extensible AI development environments.
https://github.com/TensorBlock/awesome-mcp-servers
By TensorBlock
How to Get It
None — discovery directory; browse the index at https://www.tensorblock.co/mcp or the repo's docs/*.md category files
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Help me find MCP servers that integrate with my existing development tools and workflows
Trust Signals Auto-scanned
Data & Access
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 756 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
How to evaluate tools before deploying →
Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
Scored from trust signals (evidence-eval-v1): 756 GitHub stars; contributors unknown; last commit 0d ago; license MIT.