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OpenRouter MCP Server

by lumishoang
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Python
Remote

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    • ModelShortlist

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      An MCP server that gives AI assistants current model-selection context from the OpenRouter catalog and Artificial Analysis benchmarks, enabling workload-specific recommendations with constraints like tool calling, context, price, and optional Zero Data Retention. It also helps compare candidate models and inspect catalog, benchmark, ZDR, and cache status.
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    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

    A4/5.0

    Scored across 5 tools

    Disambiguation5/5

    Each tool has a distinct purpose: get single model details, list with basic filters, search with advanced filters, compare multiple, and cache refresh. No overlapping functionality; descriptions clearly differentiate them.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern using lowercase with underscores: list_models, search_models, get_model, compare_models, refresh_cache. No mixing of conventions.

    Tool Count5/5

    With 5 tools, the server provides a focused set for model discovery and management. This is neither too few nor too many for the domain of querying model information from OpenRouter.

    Completeness5/5

    The tool set covers all essential operations for interacting with OpenRouter models: listing, searching, getting details, comparing, and cache management. No obvious gaps like missing model capability queries, as search covers those.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources