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contexte

contexte

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by contexte-tech
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Python
Hybrid

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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

    A3.8/5.0

    Scored across 5 tools

    Disambiguation5/5

    Each tool targets a clearly distinct action: search_resources finds resources, load_resource inspects one, fill_prompt renders prompt templates, download_dataset persists full datasets, and estimate_cost computes pricing. Boundaries are sharp — even the superficially similar load_resource vs download_dataset differ (preview/first rows vs full file export), so an agent can select confidently.

    Naming Consistency5/5

    All five names follow a strict snake_case verb_noun pattern (search_resources, load_resource, fill_prompt, download_dataset, estimate_cost). The only variation is plural vs singular nouns, which reflects the actual cardinality semantics rather than an inconsistency.

    Tool Count5/5

    Five tools is well-scoped for a resource-catalogue server covering discovery, inspection, prompt rendering, dataset export and cost estimation. Every tool earns its place with no redundancy.

    Completeness4/5

    The surface covers the core catalogue lifecycle: discover, inspect, use (fill prompts), export datasets and estimate cost. Minor gaps exist — no explicit browse/list-by-type or metadata filtering beyond free search, and no way to fetch a model or MCP server artifact directly — but agents can work around these via search.

    Maintenance

    ActivityMaintained
    ResponsivenessNo recent community issues

    Resources