"author:Alpha-Park" matching MCP servers:

Enables MCP-compliant clients and autonomous agents to pick optimal few-shot in-context exemplars by combining semantic token overlap, cosine similarity, and Maximal Marginal Relevance diversity ranking for prompt optimization. Runs deterministically on the Python standard library with zero external dependencies, avoiding frontier LLM round-trips for sub-millisecond selection.

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 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 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 sentence-level factual grounding verification and hallucination detection for RAG outputs via a native MCP server, providing deterministic scoring and structured telemetry.

Enables AI agents and MCP clients to decompose text into atomic claims and cross-verify them against reference knowledge to flag ungrounded claims, numerical contradictions, and hallucinated entities. Runs deterministically with zero dependencies over JSON-RPC stdio, avoiding LLM round-trips for routine verification tasks.

Enables AI agents to compress retrieved web documents against a query, pruning irrelevant noise so only the most relevant context reaches the model. It runs as a zero-dependency Python MCP server that accepts a query payload and returns structured, scored output.

Enables decomposition of complex, multi-faceted research prompts into executable query plans and synthesis DAGs, with support for scientific evidence analysis and consensus calculation.

Enables MCP clients to generate diverse hypothetical answer passages from sparse user queries, improving vector search recall by bridging lexical and semantic gaps.

Enables MCP-compatible clients to shrink oversized long-context prompts through task-relevant slicing, lexical deduplication, and entropy-preserving pruning while honoring token budgets and safeguarding system invariants.

Enables AI agents and MCP clients to combine and rerank results from multiple search or retrieval sources using Reciprocal Rank Fusion and hybrid rank fusion scoring. It exposes deterministic fusion and reranking execution over MCP with structured JSON output.