Skill MCP Server
Related Servers
Alternatives to Skill MCP Server
No user-submitted related servers found.
Related Servers
- FlicenseNot gradedqualityBmaintenanceEnables AI agents to search, browse, install, and manage a large library of skills via only 7 MCP tools, with skills stored locally and loaded on demand to minimize context overhead.4-
- AlicenseNot gradedqualityAmaintenanceEnables AI agents to search and retrieve over 89K skills on-demand at runtime, eliminating the need to manually install skills upfront.37 PyPI127MIT
- AlicenseNot gradedqualityDmaintenanceEnables LLMs to dynamically discover and execute tools through a structured skills system. Serves as a documentation hub where skills are defined in directories, allowing progressive loading and interpretation of capabilities.MIT
- AlicenseAqualityDmaintenanceConnects AI coding agents to the SkillsMP marketplace, allowing users to search, read, and install over 8,000 community-made skills. It enables agents to gain new capabilities either through on-the-spot instruction or permanent installation without requiring an API key.512 npm10MIT
- FlicenseNot gradedqualityCmaintenanceA unified skill management and distribution hub that aggregates local skill folders and exposes them via MCP protocol to AI platforms, featuring lazy loading and hot reloading.1-
- AlicenseNot gradedqualityDmaintenanceA local-first skill runtime for AI coding agents that defines skills as verifiable, composable units with sandboxed execution, and exposes them as MCP tools over HTTP.2 npm108Apache 2.0
TDQS
Scored across 7 tools
Each tool has a clearly distinct purpose with no overlap: file operations (edit/read/write) are separate from skill operations (list/load/resource/script). The descriptions reinforce unique functions, such as file_edit for search-and-replace versus file_write for creation/overwriting, and skill_script for execution versus skill_resource for reading files. An agent can easily differentiate between tools based on their specific actions and contexts.
All tools follow a consistent snake_case naming pattern with clear verb_noun structures: file_edit, file_read, file_write, list_skills, skill, skill_resource, and skill_script. The naming is predictable, using prefixes like 'file_' and 'skill_' to group related tools, making it easy for agents to understand relationships and purposes without confusion or deviation.
With 7 tools, the count is well-scoped for the server's purpose of managing files and skills. It covers essential operations like file CRUD (edit/read/write) and skill lifecycle (list/load/resource/script), providing a balanced set without being too sparse or overwhelming. Each tool earns its place by addressing a core need in the domain, avoiding redundancy or gaps.
The tool surface is nearly complete for the domain of file and skill management, covering key operations like file editing, reading, writing, skill listing, loading, resource access, and script execution. A minor gap exists in file operations, such as the lack of a delete_file tool, but agents can work around this by using file_write to overwrite or clear files. Overall, it supports core workflows effectively with no dead ends.