Narrative Graph MCP
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TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose within the Random Tree Model (RTM) workflow: creation, depth optimization, ensemble generation, and traversal. There is no overlap in functionality, making tool selection straightforward for an agent.
All tools follow a consistent 'rtm_verb_noun' naming pattern with snake_case, using descriptive verbs like 'create', 'find', 'generate', and 'traverse'. This predictability enhances readability and usability.
Four tools are well-scoped for the narrative graph modeling domain, covering core operations from tree creation to analysis. It is slightly lean but reasonable, as it focuses on essential RTM functions without unnecessary bloat.
The toolset covers key aspects of narrative tree modeling: creation, optimization, ensemble analysis, and traversal. Minor gaps may exist, such as tools for editing or deleting trees, but the core workflow is well-supported for statistical recall modeling.