A
licenseNot graded
qualityB
maintenanceExposes RAG and document intelligence pipelines as 8 composable tools for MCP-compatible clients, enabling querying, indexing, classifying, extracting, and assessing documents.
1
MIT
champollion-sulcal-mcp
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TDQS
A3.5/5.0
Scored across 17 tools
Disambiguation5/5
Each tool targets a distinct operation: launching pipeline stages (start_*), subject management (purge_subject, prune_failed_subjects), job control (get_job_status, list_jobs, cancel_job, get_job_log), and pipeline info (get_pipeline_info, preflight_check). Minimal overlap; even similar tasks like subject removal are differentiated by scope.
Naming Consistency4/5
Most tools follow a consistent pattern: launch verbs (start_*) for pipeline stages, get_* for retrieval, and descriptive verbs for actions. Deviations include 'purge_subject' vs. 'prune_failed_subjects' (different delete verbs) and 'preflight_check' (no prefix), but overall naming is clear and predictable.
Tool Count5/5
17 tools is appropriate for a multi-stage neuroimaging pipeline with job management and utility functions. Each tool earns its place, covering all stages and supporting operations without being excessive or sparse.
Completeness5/5
The tool set covers the full pipeline lifecycle: stage launches, job monitoring, subject cleanup, and configuration checks. All essential operations are present with no obvious gaps for the stated purpose.
Maintenance
ActivitySlowing
ResponsivenessNo issues