world-model-mcp
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Alternatives to world-model-mcp
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Related Servers
- AlicenseNot gradedqualityDmaintenanceProvides AI coding agents with pre-edit situational awareness by combining structural call graphs and co-change history to prevent incomplete edits. It surfaces files that historically change together, reducing missed coupled modules.3MIT
- AlicenseAqualityBmaintenanceEnables AI agents to maintain a persistent knowledge graph of a project, providing dependency context, impact analysis, side-effect discovery, and session recording for more informed coding decisions.5MIT
- AlicenseNot gradedqualityBmaintenanceEnables coding agents to query a local, versioned knowledge graph of a software project, retrieving overviews, context packs, evidence, and explanations to make informed changes.MIT
- FlicenseBqualityBmaintenanceTurns local code repositories into a queryable dependency graph built from AST parsing and git co-edit history, then exposes it alongside an Obsidian vault as persistent memory. Enables context retrieval, impact analysis of proposed changes, and curation of durable prose knowledge through full-text search and Personalized PageRank classification—all fully offline without API keys or embeddings.30-
- AlicenseNot gradedqualityAmaintenanceA temporal knowledge graph system that enables users to record and query architectural decisions, implementation patterns, and project failures. It integrates with Claude to provide hybrid search, timeline tracking, and automated knowledge gap detection using graph analysis.4MIT
- FlicenseNot gradedqualityAmaintenanceMemtrace is a persistent memory layer for coding agents, built as a bi‑temporal structural knowledge graph over your codebase (AST‑driven symbols and relationships, plus temporal evolution and cross‑service API topology)471-
TDQS
Scored across 31 tools
Several tools have poorly separated boundaries: validate_change, simulate_change, predict_regression, and predict_test_failures all appear to assess the impact of a proposed change, differing mainly in subtle emphasis. Similarly, query_fact, search_global, and get_context_for_action overlap heavily in fact retrieval, and record_correction, record_decision, and pin_annotation all capture human feedback. An agent would frequently struggle to select the right tool without reading every description.
Every tool follows a consistent snake_case verb_noun pattern, e.g., get_constraints, record_event, predict_regression, prove_entry_inclusion. Even the more unusual names like pin_annotation and seed_project fit the same imperative structure. This is a highly predictable and uniform naming convention.
31 tools is well beyond the 25+ threshold for a single server and will overwhelm tool selection, especially given the many overlapping prediction and retrieval tools. The server would be more coherent with roughly half the current surface area, consolidating related reads and writes into broader commands.
The domain is broadly covered: it supports knowledge-graph population and queries, event and decision recording, constraint ingestion and validation, regression prediction, audit-log integrity, compaction auditing, and context export. Minor gaps exist, such as no explicit fact/constraint update or delete lifecycle and no project listing tool, but the core workflows are well supported.