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Related Servers

Alternatives to smart-router

No user-submitted related servers found.

    Related Servers

    • A
      license
      A
      quality
      C
      maintenance
      Puts a single MCP endpoint in front of many downstream MCP servers, routing each prompt to the right server and tools so an agent sees a few relevant tools instead of every schema. Reads run automatically, writes require explicit user confirmation via elicitation, and consequential calls are blocked by a risk gate that logs every routing decision and call.
      3
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Acts as a proxy/router for multiple downstream MCP servers, exposing only meta-tools to the host to reduce token usage, enabling efficient search and invocation of tools from a fleet of servers.
      7 npm
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Fronts every MCP server you have with two tools: search shortlists relevant tools, and execute runs sandboxed Python that calls upstream tools as mcp..(). It enables agents to make bulk, repeated, or conditional tool calls using calibrated decision-model choices instead of per-item LLM generation.
      3
      MIT
    • A
      license
      A
      quality
      B
      maintenance
      Aggregates multiple MCP servers into a single interface, reducing token overhead and simplifying tool management for LLMs.
      6
      80 npm
      18
      Apache 2.0
    • A
      license
      Not graded
      quality
      C
      maintenance
      An MCP aggregator that consolidates multiple MCP servers behind a single interface with just 3 tools (search, get details, execute), reducing context pollution for AI agents by avoiding direct exposure of numerous tool schemas.
      48 npm
      2
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI clients to access many upstream MCP servers and skills through five progressive-disclosure tools, searching and loading only the tool schemas or skill instructions needed on demand. This keeps context small for local and small models while still allowing them to browse, describe, and call tools or load skills.
      MIT

    TDQS

    A4/5.0

    Scored across 5 tools

    Disambiguation5/5

    Each tool serves a distinct role: search for discovery, call_tool for execution, use_skill for loading skill instructions, route for recommendations without execution, and admin for registry management. There is no functional overlap; an agent can clearly differentiate when to use each.

    Naming Consistency4/5

    All names are lowercase snake_case and readable, but they mix bare verbs (search, route) with verb_noun forms (call_tool, use_skill) and a noun (admin). This is a minor deviation from a fully consistent pattern, though the intent remains clear.

    Tool Count5/5

    With only 5 tools, the server is tightly scoped to its routing/discovery purpose. Each tool earns its place, covering search, invocation, skill handling, recommendation, and administration without unnecessary bloat.

    Completeness5/5

    The surface fully covers the smart-router domain: agents can discover tools/skills, invoke them, load skill instructions, get routing recommendations, and manage the registry. No obvious dead ends or missing lifecycle operations for the stated purpose.

    Maintenance

    ActivityInactive
    ResponsivenessOutdated data