A
licenseA
qualityB
maintenanceOffloads cheap work from cloud LLM agents to a local Ollama model, reducing costs and keeping frontier models focused on complex tasks.
8
34 PyPI
3
MIT
Local Worker MCP
by CaioAllgayer
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TDQS
B3.4/5.0
Scored across 8 tools
Disambiguation5/5
Each tool has a clear, distinct purpose: health check, task delegation, batch processing, file reading, PDF analysis, cache statistics, cleanup, and clearing. There is no overlap in functionality, making it easy for an agent to select the appropriate tool.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern (e.g., local_status, delegate_task, cache_stats). The naming is predictable and clear, with verbs like 'delegate' and 'cache' used consistently to indicate the action and domain.
Tool Count4/5
With 8 tools, the count is appropriate for the server's purpose of managing a local worker and its cache. The scope is well-defined without being too thin or overly broad.
Completeness4/5
The tool surface covers the key operations: health checks, task delegation (including batch and file processing), and cache management (stats, cleanup, clear). A minor gap is the lack of a tool to update or cancel a delegated task, but the core workflows are well supported.
Maintenance
ActivitySlowing
ResponsivenessNo issues