"PlayStation Portable" matching MCP servers:

Enables AI agents to search, fetch, and apply portable engineering standards over MCP, keyless and offline.

A portable, self-hosted semantic memory layer for LLM agents, enabling them to search and add memories by meaning over the Model Context Protocol.

Enables AI agents to share a portable, user-level memory layer through MCP, allowing them to store, search, update, link, and consolidate facts with optional full-text and vector retrieval.

Portable MCP server for storing, searching, and verifying compressed LLM context without losing invariants.

Enables read-only access to a local-first catalog of portable agent skills, exposing tools to discover ranked matches, inspect stored artifacts, traverse declared relationships, and view configuration.

Enables users to create and control portable, user-owned memory archives, including saving memories, reflections, values, and stories, exporting them as open JSON, publishing or discovering public archives, and loading consented material as a clearly-labelled memory-based AI persona.

Portable, provider-agnostic memory for AI agents using Azure Data Explorer (Kusto) with temporal and semantic scoring.

MCP server that provides a portable personal knowledge layer for AI tools, enabling cross-LLM continuity and personalized context across clients like Claude, ChatGPT, and Cursor.

Enables MCP-speaking agents to read and write a local, portable, JSONL-based long-term memory, so context and durable facts persist across sessions through search, context injection, and memory lifecycle tools.

A local MCP server that gives AI copilots a shared, portable long-term memory backed by an embedded knowledge graph, with semantic search and export/import capabilities.

Centralized repository for modular, portable skills and memory, enabling AI agents and IDEs to access personalized tools and memory via stdio or SSE transport.

Enables any MCP client (zcode, Claude Code, Cursor) to give its model bi-temporal long-term memory, auditable evidence-cited retrieval, LLM-assisted document extraction, and deterministic A* skill planning with precondition/effect verification. All four capabilities share one portable SQLite store and can be mounted independently on demand.

A portable self-hosted memory layer for AI tools, storing context, memories, and handoffs for access from any MCP-compatible client.

Local-first, MCP-first cross-agent cognitive asset layer that enables agents to store, retrieve, govern, and migrate memory, roles, and verified skills as portable assets via MCP tools and SQLite.

An MCP server aimed to be portable, local, easy and convenient to support semantic/graph based retrieval of txtai "all in one" embeddings database. Any txtai embeddings db in tar.gz form can be loaded

Enables agents to import selected conversation archives, search and edit an Obsidian-compatible Markdown memory wiki, and read/write shared context across AI clients through MCP.

Enables MCP-capable AI agents to recall from and write to an encrypted, portable memory vault over stdio, with search, context packing, stats, verification, and forget operations.

FEGIS is a Model Context Protocol server that gives LLMs structured, persistent and portable memory through customizable cognitive tools defined in schema.

Enables coding agents to share a local, user-owned AI work identity across MCP-compatible tools by storing inspectable and editable preferences, lessons, decisions, playbooks, and project context. It lets users review, approve, or override what agents can remember and load, providing portable continuity without a cloud account.

Enables AI agents to maintain persistent personal memory and portable skills across MCP-compliant clients, with hybrid semantic recall and deterministic SQL analytics.

Gives AI agents a local, portable long-term memory store they can read and write over MCP: original sources are kept while distilled, quote-backed knowledge is added, searched, superseded and versioned. Results label each item as current, superseded, disputed, an open question, or insufficient evidence, and conflicting updates go to review instead of silently overwriting confirmed conclusions.

Enables MCP clients to teach a portable brain file, resolve requests into instant facts, deterministic tool calls, or external-model fallbacks, and progressively learn unknown answers. It supports embedding an AI memory/router that works offline for known queries and routes only novel or semantic needs to configured models.

Exposes a local SQLite-backed long-term memory to AI agents as MCP tools, letting them add conversations and documents, record dated facts with provenance, search and assemble context, and edit, inspect, or forget what was stored. It runs with your own embedding and LLM keys, so memories stay readable, exportable, and portable as plain markdown.

Provides AI-powered code intelligence for any codebase using local LLMs and vector search, enabling semantic code search, pattern analysis, and context-optimized code generation with 90% token savings.

Enables users to scrape and index documentation and query it through MCP, a CLI, and a web UI using local multilingual vector embeddings, with no API keys and offline operation after setup.

Enables AI coding tools to share a single portable, local-first identity layer — your preferences, quality bar, lessons, decisions, and project context — stored as inspectable local files rather than hidden cloud memory. Lets you preview, edit, approve, or override exactly what any connected client receives, so context carries across Claude Code, Codex, Cursor, Windsurf, and other MCP-compatible tools.

Enables MCP-compatible coding tools to start from one portable, locally stored work-identity layer, so preferences, quality standards, lessons, decisions, and project context carry across Claude Code, Codex, Cursor, Windsurf, and similar clients instead of being siloed per tool. Users keep that knowledge as inspectable JSON/Markdown files they can preview, approve, edit, or override, with no cloud account required.

Enables MCP-compatible coding tools to start from one portable, local-first identity and knowledge layer containing the user's approved preferences, quality standards, lessons, decisions, and project context. It stores this in local files the user owns and can inspect, edit, or override, with governance over AI-proposed writes and no cloud account required.

Enables MCP-compatible coding agents to start from one portable, local-first identity and long-term knowledge layer, so preferences, quality bars, lessons, decisions, and project context carry across tools instead of being relearned per session. Everything the AI writes stays in local files the user can inspect, edit, approve, or override.

Enables MCP-compatible coding tools to share a portable, local-first AI work identity and memory layer, so preferences, decisions, lessons, and project context persist across sessions and clients. Users can inspect, edit, approve, and override what the AI receives, keeping the store on their own machine without a cloud account.