"namespace:91.174.133.8" matching MCP servers:

Enables AI agents to inspect and operate a Kubernetes cluster safely, with read-only mode and namespace allowlist for mutations.

MCP server providing persistent memory management for AI agents using SQLite and FTS5, enabling storage, full-text search, and recall of memories with namespace isolation.

Enables connecting multiple remote HTTP MCP servers through a single local stdio endpoint, handling OAuth authentication, token refresh, and namespace-scoped tools.

Enables natural language querying of Apache Iceberg lakehouse by exposing typed tools for namespace discovery, table metadata inspection, snapshot history, time travel SQL generation, and partition pruning explanation.

Provides agent-scoped persistent storage with namespace isolation and TTL-based retention tiers. Includes a full audit trail and is backed by a single SQLite file.

Embedded, local-first agent memory: facts extracted into a per-namespace SQLite file (vec0 + FTS5) with hybrid retrieval and point-in-time (time-travel) queries. ADD-only history over stdio — no server process, no cloud dependency.

Enables AI agents to securely read and write SAP ABAP development objects via the ADT REST API, enforcing policy-based guardrails such as package/namespace allow-lists, read-only mode, human approvals, and audit logging.

Enables driving Omniverse Kit apps (Isaac Sim, Isaac Lab) over MCP, allowing agents to control simulations, run Python, and call namespace-scoped tools via a single bridge.

Enables interactive Python execution with a persistent IPython kernel through MCP, retaining namespace state and providing structured output logs for agent and tool integrations.

Enables coding agents to store, search, update, and delete durable facts, preferences, decisions, and plans in a shared local SQLite database, along with per-namespace profile get/set and stats. Uses offline lexical FTS5 search so multiple agents working on the same project can share one memory store.

Enables MCP clients to store, recall, correct, and forget knowledge with per-sentence source bindings and a replayable retrieval traceId, so every answer can be traced to a specific document passage. Adds document-level version chains that retire superseded content from default retrieval, tombstone-based forgetting, three-layer user/namespace/scope isolation with full operation auditing, and deterministic calculator/date tools.

Enables access to Yuque (语雀) documentation through cookie-based authentication, supporting document retrieval, search, TOC navigation, and content management from knowledge bases via URL or namespace.

Ships only the MCP server, not the recorder. Recording works by wrapping a process on the host — a local proxy, a PATH shim, a shadow git index. None of that survives a container boundary, so orca record inside here would watch an empty namespace. What does travel is the read side: the six MCP tools serve runs that already exist on disk, so a client mounts its .orca directory and r

Enables MCP clients to add, search, and delete persistent namespace-scoped notes through a local stdio server backed by a SQLite database. Namespaces isolate queries and content-derived IDs make repeated writes idempotent.

Enables AI agents and any MCP-compatible client to persist, search, update, and delete plain-text memories across sessions, using BM25 lexical ranking over SQLite FTS5 with namespace filtering, tags, free-form metadata, and JSONL export/import — entirely local, with zero external infrastructure.

Enables multiple AI agents to share a self-hosted, namespace-scoped cloud memory via a local stdio MCP server that caches valid results locally in SQLite and syncs to a remote MySQL backend. Exposes tools for searching, retrieving, timeline browsing, remembering, forgetting, and syncing memories with versioning, conflict handling, and redaction.

A read-only MCP server that gives AI assistants Roslyn-powered browsing of any C# solution, letting them list types by folder or namespace, read XML documentation, and retrieve method signatures, locations and bodies. This keeps queries targeted so agents pull only the exact slice of source they need instead of reading whole files into context.