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Bagel

by Extelligence-ai
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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

A4/5.0

Scored across 25 tools

Disambiguation5/5

Each tool targets a distinct resource or action: capabilities, pipelines, data-source inspection, live-topic subscriptions, and exports are all clearly separated. Similar-sounding tools such as run_poml_capability versus run_pipeline, list_pipelines versus list_pipeline_capabilities, and query_messages versus read_loggings are explicitly cross-referenced and disambiguated in their descriptions.

Naming Consistency4/5

The set overwhelmingly follows a verb_noun snake_case pattern with consistent list/save/delete/run/preview/export verbs. Minor deviations include run_poml_capability supporting Markdown despite the POML-specific name, delete_capability breaking the save_agent_capability/list_agent_capabilities symmetry, and the somewhat vague snap_hardware verb.

Tool Count3/5

With 25 tools the server sits exactly in the heavy 16-25 range, making it borderline even though the domain is fairly broad. The pipeline and capability management clusters plus the four exporter tools add significant surface area, though none of the tools feel entirely redundant.

Completeness4/5

Core lifecycles are covered well: capabilities have list/save/run/delete, pipelines have save/get/list/delete/run/batch/preview, and data inspection covers schemas, SQL queries, and textual logs. Minor gaps remain, such as no tool to enumerate recorded data sources or produce byte-preserving native bag exports, but agents can work around these.

Maintenance

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