Skip to main content
Glama
Flowtrica

Progressive Skills MCP

by Flowtrica
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Related Servers

Alternatives to Progressive Skills MCP

No user-submitted related servers found.

    Related Servers

    • clair

      concensure
      F
      license
      Not graded
      quality
      D
      maintenance
      An MCP server that reduces token usage by lazily loading skills and tools only when needed, and routing repetitive subtasks to ML backends instead of the LLM.
      -

    • Skill Agent Gateway

      YanShawn
      F
      license
      Not graded
      quality
      D
      maintenance
      An MCP server that provides intelligent discovery, search, and on-demand loading of Claude Code skills and agents, reducing token usage by lazy loading.
      -

    • AgentSkill MCP

      QianjieTech
      A
      license
      Not graded
      quality
      D
      maintenance
      A universal MCP server that enables any MCP-compatible agent to use Anthropic's Claude Agent Skills with progressive disclosure, reducing context overhead while maximizing capability.
      47
      Apache 2.0

    • Code Executor MCP Server

      aberemia24
      A
      license
      Not graded
      quality
      D
      maintenance
      Universal MCP server for executing TypeScript and Python code with progressive disclosure, reducing token usage by 98% by enabling on-demand access to all other MCP tools through code execution rather than loading tool definitions directly.
      7 npm
      130
      MIT
    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    A4.1/5.0

    Scored across 4 tools

    Disambiguation5/5

    Each tool targets a distinct action: loading the main skill, reading a specific file, listing files, and fetching resources as a fallback. The purposes are clearly separated with specific use-case guidance, so an agent can reliably choose the right one.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern (fetch_resource, load_skill, read_skill_file, list_skill_files) with no mixed conventions or vague verbs. The naming is predictable and matches the operation performed.

    Tool Count5/5

    Four tools is well-scoped for a skill management server: loading, reading, listing, and fetching resources. Each tool earns its place without redundancy, and the count is neither sparse nor excessive for the domain.

    Completeness4/5

    The set covers the core operations needed to interact with skills (load, read, list, fetch). The only potential gap is the absence of a dedicated 'list all skills' tool, but skills are advertised in the system prompt, so this is a minor gap that agents can work around.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources