LLM MCP Bridge
Related Servers
Alternatives to LLM MCP Bridge
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityNot gradedmaintenanceEnables benchmarking of Large Language Model APIs by measuring performance metrics such as generation throughput, prompt throughput, and Time To First Token (TTFT) with configurable concurrency levels and parameters.1MIT
- AlicenseAqualityDmaintenanceEnables AI agents to connect to and chat with multiple LLM models (OpenAI, OpenRouter, custom endpoints) with conversation history management and model switching capabilities.314 npm2MIT
- AlicenseNot gradedqualityAmaintenanceEnables AI agents to route chat completion requests to task-appropriate models via LiteLLM/OpenRouter, track usage and budgets, and run side-by-side eval comparisons of models based on cost, latency, and response quality.MIT
- AlicenseNot gradedqualityAmaintenanceEnables provider-agnostic LLM discovery, capability testing, stability screening, and AIPerf benchmarking through MCP, with persistent SQLite history.41 PyPIMIT
- AlicenseNot gradedqualityAmaintenanceAggregates dozens of pre-built connectors into one OpenAI-compatible endpoint for AI clients, with a web UI for installing, configuring, monitoring, and managing them.409 npmMIT
- AlicenseAqualityBmaintenanceEnables any AI agent to connect to any OpenAI-compatible LLM provider through a unified MCP interface, with automatic failover, caching, cost tracking, and multi-model consensus.13MIT
TDQS
Scored across 8 tools
Most tools have distinct purposes like benchmarking, chatting, and comparing models, but there is overlap between llm_get_models and llm_status, as both list available models. The descriptions help differentiate them, but an agent might still be confused about which to use for model listing.
All tool names follow a consistent 'llm_' prefix with descriptive suffixes in snake_case, such as llm_benchmark and llm_chat. This predictable pattern makes it easy for agents to understand and navigate the toolset without confusion.
With 8 tools, the count is well-scoped for evaluating and testing LLM models. Each tool serves a specific function in performance analysis, quality assessment, and model management, fitting the server's purpose without being overwhelming or insufficient.
The toolset covers key aspects of LLM evaluation, including benchmarking, chatting, model comparison, coherence testing, and capability assessment. A minor gap is the lack of tools for model configuration or fine-tuning, but core workflows are well-covered for quality and performance analysis.