A
licenseB
qualityA
maintenanceEnables AI agents to act as a state-driven tutoring engine over MCP, with server-side mastery estimation, prerequisite checks, evidence-based assessment, and FSRS spaced review scheduling.
58
1
MIT
Knowledge Forest MCP
by znecho9
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TDQS
A3.9/5.0
Scored across 10 tools
Disambiguation4/5
Tools are intentionally separated by task, and descriptions carefully disambiguate state-changing actions, e.g. update_node_learning_state cannot mark mastery and record_verification is the only mastery mechanism. Minor overlap remains between forest_overview and get_learning_queue, and get_node_context already includes diagnosis alongside the dedicated diagnose_node tool, so an agent could occasionally pause between them.
Naming Consistency4/5
The naming convention is strongly consistent: snake_case verb_noun pairs such as search_knowledge, create_goal_tree, record_verification, diagnose_node, and export_forest dominate the set. forest_overview is the only outlier as a noun_noun name and does not follow the imperative verb pattern used elsewhere, which keeps this from a perfect score.
Tool Count5/5
Ten tools map cleanly to the full knowledge-forest workflow: orient, search, create, update learning state, append evidence, verify mastery, queue readiness, diagnose, and export. The count is well within the ideal range, with no redundant utilities or token bloat relative to the server's purpose.
Completeness5/5
The set covers the full learning lifecycle for its stated domain: creating goal trees, reading node context, updating non-verified workflow state, appending source-visible notes, recording verification attempts, queueing ready work, diagnosing gaps, and exporting complete portable state. Missing delete or goal-tree editing operations are not obvious gaps because the design is explicitly append-only and evidence-focused, and the tools explicitly prevent dead ends between state mutations.
Maintenance
ActivityMaintained
ResponsivenessNo issues