"HAL" matching MCP servers:

HAL (HTTP API Layer) is a Model Context Protocol (MCP) server that provides HTTP API capabilities to Large Language Models.

Enables AI agents to store, recall, reinforce, and manage local long-term memories such as preferences, procedures, facts, episodes, and traces, with fading and archiving over time. It works with MCP clients via stdio or local HTTP while keeping data on the user's machine.

Enables to search and retrieve chunks from a fictional library's documentation through three tools (kb_search, kb_fetch, kb_sources), allowing LLMs to perform RAG queries via the Model Context Protocol.

This read-only MCP Server allows you to connect to HCL Domino data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out the first managed MCP platform: CData Connect AI (https://www.cdata.com/ai/).

Enables deterministic auditing of RAG retrieval faithfulness and hallucination scores, providing structured JSON output for AI agents and MCP-compliant clients.

Enables sentence-level factual grounding verification and hallucination detection for RAG outputs via MCP, with deterministic scoring and structured telemetry.

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

Enables autonomous agents and MCP clients to decompose text into atomic claims and verify them against reference knowledge, detecting ungrounded claims, numerical contradictions, and hallucinated entities.

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.

MCP-native auditor for LLM hallucination and grounding issues in RAG systems. Provides prioritized findings in table, JSON, or SARIF format for CI gating and AI agent integration.

Gives AI agents governed, provenance-tagged memory where facts are invalidated rather than deleted and every recall result carries validity dates, confidence, and replacement links. Stores memory in a local SQLite file with tools for remembering, recalling, invalidating, and pinning facts, plus policy enforcement and an audit trail.

Enables Claude Code, Codex and other MCP agents to share one bounded, persistent memory across sessions, so a session starts from what earlier sessions learned instead of re-reading the repository. Storage and retrieval run locally on SQLite full-text search with per-client tokens and scopes, and optional local model enrichment, without any paid model in the path.

A permissioned memory tree for you and your AI — served over MCP.

Gives Google Antigravity persistent memory by automatically recalling profile and project context before model calls and saving new conversation turns at turn boundaries. Exposes MCP tools to add, search, list, profile, and forget memories, with privacy filtering and per-repository scoping.

An evidence-first Model Context Protocol server for private business documents and structured datasets, preserving original files and source lineage.