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Algenta MCP

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by thyn-ai
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    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

    A3.8/5.0

    Scored across 140 tools

    Disambiguation2/5

    There are several clusters of tools with unclear boundaries, most notably the decision/simulation family (simulate, plan_decision, product_decision, score, compare, recommend) and the dataset onboarding variants (onboard_dataset, connect_data, register_source, list_data vs list_datasets). Even with detailed descriptions, an agent will frequently struggle to pick the right tool among these overlapping options.

    Naming Consistency4/5

    The vast majority of tools follow a clear verb_noun snake_case pattern (get_, list_, create_, update_, delete_, run_, simulate_), which is quite consistent across a huge surface. The main deviations are bare single-word tools like simulate, score, compare, batch, recommend, tokenize, and rerank, plus a few odd names like responses and get_me, but these are minor relative to the overall pattern.

    Tool Count1/5

    140 tools is an extreme count for a single MCP server, far beyond even the 50+ threshold for the lowest score. The surface spans billing, deployment, data connectors, repository intelligence, LLM utilities, simulation, agent runs, team management, audit, triggers, and more, making it impractical for an agent to discover and select tools efficiently.

    Completeness4/5

    Within its sprawling scope, the server covers most lifecycle operations well: connectors, API keys, team members, agent runs, triggers, decisions, jobs, and deployments all have create/read/update/delete or equivalent coverage. There are minor gaps such as no way to update a trigger's condition or rename a dataset, but agents can generally work around these.

    Maintenance

    ActivityActive
    ResponsivenessUnresponsive

    Resources