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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

A3.9/5.0

Scored across 18 tools

Disambiguation4/5

Most tools have clearly distinct purposes, and the naming and descriptions usually make the target resource explicit (e.g., describe_data_source vs describe_topic). Some overlap exists: run_pipeline vs run_pipeline_batch and read_loggings vs query_messages could be confused, though the descriptions clarify the batch/query angle.

Naming Consistency5/5

Every tool follows a consistent lowercase snake_case verb_noun or verb_for_target pattern. Groups like list_*, export_for_*, and describe_* are immediately recognizable, and there is no mixing of conventions.

Tool Count3/5

18 tools is on the heavy side for a single server, but the count is not unreasonable given the breadth (discovery, pipeline authoring, live subscription, exports, hardware). The four export_for_* tools feel redundant as a set, which pushes this from 'slightly heavy' to 'borderline heavy'.

Completeness4/5

The main workflow is well covered: discover sources/topics, inspect messages, preview/save/run pipelines, subscribe to live topics, and export events to multiple viewers. Minor gaps remain, such as no tool to list, update, or delete saved pipelines and no unsubscribe/stop mechanism for standing subscriptions.

Maintenance

ActivityActive
ResponsivenessResponsive

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