"author:Alpha-Park" matching MCP servers:

Enables agents to build and query structured semantic graphs, extracting, traversing and multi-hop reasoning over entity-relation-object triples while producing Cypher and JSON-LD graph output. It also combines BM25 lexical and dense vector retrieval with rank fusion, semantic caching and context reordering for grounded agentic memory.

Enables autonomous agents to snapshot execution-tree and context states, evaluate branch viability, and roll back to previous checkpoints when an exploration path fails without restarting from scratch.

Enables agents to evaluate multi-turn episodic memory buffers, compute multi-factor salience scores, and deterministically prune low-utility noise so retained context fits within model limits.

Enables agents to run hybrid retrieval, reciprocal rank fusion, knowledge graph expansion, semantic caching, and context reordering so critical evidence is placed at prompt boundaries to reduce lost-in-the-middle degradation.

Enables agents to ground claims in enterprise knowledge by hashing source chunks, tracing claims back to their origins, validating clearance boundaries, and maintaining audit trails to support anti-hallucination workflows.

Enables personal AI agents to fuse ambient multimodal perception with continuous episodic memory streams, routing routine reflexive actions at low latency while reserving heavier reasoning for ambiguous plans. Memory records apply Ebbinghaus recency decay with contradiction resolution and user-controlled deletion.

Enables personal AI agents to route routine, reflexive decisions through fast deterministic System-1 cognition while reserving frontier LLM calls for ambiguous multi-hop plans. Combines ambient perception, decayed episodic memory with contradiction resolution, and proactive sensing behind reversible execution checkpoints, token budgets, and local-first memory retention.

Enables agents to retrieve relevant context via a zero-dependency hybrid search engine that blends sparse keyword-based lexical ranking with dense vector similarity scores into one fused result set, then refines it with knowledge graph triplet expansion and lost-in-the-middle context reordering. It also provides episodic memory consolidation, recency decay, and semantic query caching, exposed as a JSON-RPC 2.0 stdio MCP server for clients such as Claude Desktop, Cursor, and Windsurf.

Enables personal AI agents to detect user attention cues, compute intent probabilities, and execute guarded dry-run actions through MCP, with low-latency reflex routing, episodic memory, and safety guardrails.

Enables agents to decay stale transient interactions and consolidate frequent episodic events into enduring semantic user persona traits using sleep-cycle and forgetting-curve inspired memory processing.

Enables MCP-compatible agents and IDEs to retrieve episodic long-term memories using hybrid BM25 keyword search, semantic n-gram cosine similarity, and recency decay ranking, with deterministic zero-dependency execution.

Enables agents to store LLM query-response pairs and retrieve them via fuzzy cosine similarity matching, reducing duplicate inference calls; it exposes tools for setting cache entries, semantic lookup, and hit-rate statistics.

Enables AI agents to analyze and link citations across multiple research sources, building cross-citation graphs and resolving consensus from conflicting findings. It exposes this capability as a deterministic, zero-dependency MCP server that plugs into Claude Desktop, Cursor, and other MCP-compliant clients.

Enables LLM clients to manage hierarchical working, dialogue, and episodic memories with decay-aware consolidation, hybrid lexical/vector retrieval, knowledge-graph expansion, and semantic caching over MCP.

Enables agents to cache and deduplicate semantic queries using exact SHA-256 matching and cosine similarity thresholds with LRU eviction, reducing redundant LLM calls and improving throughput.

Enables agents to resolve routine typed routing, loop-stall arbitration, and tool safety checks locally instead of invoking a frontier LLM, returning schema-enforced decision outputs and telemetry at sub-millisecond latency. Also provides reversible execution checkpoints, token budgets, and decay-based episodic memory for safe, privacy-preserving autonomous operation.

Enables agents to compile a personal knowledge vault into a linked graph by indexing bi-directional links and parsing [[wikilinks]], tags, and unlinked mentions across notes. It runs deterministically over MCP without external dependencies, turning raw note collections into navigable, queryable knowledge structures.

Enables agents to encrypt and sanitize sensitive data such as credentials, credit card numbers, and addresses locally before dispatching prompts to cloud LLMs, then rehydrate the masked values client-side. It runs deterministically with zero external dependencies over MCP JSON-RPC stdio, so no plaintext PII leaves the user's machine.

Enables parsing of messy multi-turn Slack and WeChat Work troubleshooting threads into validated Q&A resolution pairs, stripping duplicate organizational knowledge and synthesizing structured Markdown SOP runbooks. Exposes this curation capability as MCP tools over JSON-RPC 2.0 stdio, with zero external dependencies.

Provides a zero-dependency MCP interface that equips personal AI agents with agenda tracking, autonomous sandbox execution, ambient cue handling, fast reflex intent processing, and episodic memory.