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Decision brief
awesome-mcp-servers is a curated list of Model Context Protocol servers for AI applications, useful when you need specific resources related to MCP and less suitable if your focus is on other protocols.
Good fit when
- Use awesome-mcp-servers if you specifically require access to detailed listings of Model Context Protocol server resources that can be integrated into AI applications.
- Consider this tool for research or development projects where the integration and testing of various MCP servers are crucial.
Avoid when
- Avoid using it if your requirements do not align with the Model Context Protocol, opting instead for platforms that support a broader range of protocols.
- If you need to focus on different aspects of AI development not covered by MCP resources or prefer real-time data over curated lists, this might not be suitable.
- Pricing:
- unknown - The pricing model is unknown since the information provided does not specify any costs associated with using awesome-mcp-servers.
- Requirements:
- Requires familiarity with Model Context Protocol servers for effective use.; May require additional software or setup configurations to fully utilize the listed server resources.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Archived (111d since push)
- As of today
- Provenance
- Not a fork · Personal account
- As of today
- Security (OSV)
- No lockfile
- As of 3w
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/appcypher/awesome-mcp-serversHow it fits your stack(1)
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Relationship graph
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Similar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Provides a compiled collection of MCP server resources for AI applications.
Capability facts
No sourced capability facts yet. Facts appear after ingest scans repo manifests (Dockerfile, package.json, MCP configs).
Categories
Tags
README
License
To the extent possible under law, Stephen Akinyemi has waived all copyright and related or neighboring rights to this work.
For agents
This page has a .md twin and JSON over the API.