Toolport vs Unabyss: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Toolport and Unabyss — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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Toolport
Toolport
Free open-source local MCP gateway. Set up each server once and share it across Claude, Cursor, VS Code, Codex, Windsurf.
Key features
- Universal MCP gateway: Set up any MCP server once and every agent (Claude, Cursor, VS Code, Windsurf, Codex, Antigravity) shares it with hot toggles and no restarts.
- Lazy tool discovery: Exposes a handful of meta-tools instead of dumping hundreds of tool definitions, cutting tool-definition tokens 74–91% at the same task success on a frontier model.
- Tool integrity checks: Fingerprints every tool and flags rug-pulls (a definition changing after approval) and tool poisoning (hidden instructions in descriptions), on by default and entirely local.
- Keychain secrets: API keys live in your OS keychain and are injected at runtime — never in a config file, never in the cloud.
- Per-tool governance: Toggle any tool on or off with one switch to hide destructive tools from every agent fleet-wide.
- Live observability: Per-server latency, error rates, and a full audit trail of every tool call built into the app.
- Cross-platform local runtime: Runs on Windows, macOS, and Linux with no account and no cloud dependency, released under the MIT license on GitHub.
- Toolport for Teams: Shared governed set of MCP servers for a whole team (free for up to 5 people) while each person's API keys stay on their own machine.
Best for
- Individual AI power users running Claude, Cursor, and Codex who want a single place to configure MCP servers instead of pasting the same setup into each agent.
- Engineers hitting context-window limits from bloated tool-definition tokens who need lazy discovery to keep long agent sessions cheap and sharp.
- Security-minded developers who need API keys stored in the OS keychain and cryptographic integrity checks against tool poisoning and definition rug-pulls.
- Small teams (up to 5 people) who want one governed catalog of MCP servers while keeping each engineer's credentials on their own machine.
- Agent-tooling authors who want a local audit trail of latency, error rates, and every tool invocation across servers for debugging.
- Ops leads applying per-tool governance to hide destructive actions (writes, deletes) from every agent with a single fleet-wide toggle.
Unabyss
Unabyss
Self-updating universal context layer that provides segmented, persistent context to agents and LLMs via the MCP connector protocol.
Key features
- Self-Updating Context Layer: Continuously ingests and refreshes relevant documents, events, and interaction history so connected agents always receive current context without manual updates.
- MCP-Native Connector: Exposes context through the MCP connector protocol, enabling any MCP-capable agent or LLM to request and consume the same shared context surface.
- Segmented Access Controls: Context is segmented by default to enforce boundaries between projects, users, or data classes, reducing accidental exposure of private information.
- Persistent Cross-Session Memory: Stores and surfaces long-lived context across sessions, addressing short-lived model memory and improving multi-step task continuity.
- Automatic Context Prioritization: Selects and supplies the most relevant context for a given prompt or agent task, reducing prompt size and minimizing irrelevant data sent to models.
- Agent-Agnostic Integration: Works with multiple agents and LLM backends (via MCP), allowing teams to centralize context management without coupling to a single model provider.
- Persistent, session-spanning context storage to address short-term memory limits
- Self-updating context that automatically evolves without manual prompt engineering
- MCP-native connectivity to expose context to any MCP-compatible agent or LLM
- Default segmentation of context to isolate scopes or subjects
- Automated context refresh to keep agent inputs current across sessions
- Designed as an infrastructure layer for agent ecosystems (reduces repeated context provisioning)
Best for
- Multi-Session Agent Workflows: Enable assistants and agents to resume work across days by providing persistent project context, previous decisions, and relevant files automatically.
- Developer Tools and Code Assistants: Feed up-to-date repo context, recent commits, and issue threads to coding agents so they produce more accurate code suggestions and fewer out-of-context answers.
- Customer Support Augmentation: Supply conversation history, ticket metadata, and product docs to support agents so responses stay consistent across handoffs and follow-ups.
- Long-Running Automation: Power workflows that span hours or days (e.g., data collection, review cycles) by keeping the automation engine informed of evolving inputs and state.
- Cross-Agent Coordination: Share a canonical context layer between specialized agents (search, summarization, planner) so each agent works from the same authoritative source.
- Privacy-Aware Context Sharing: Use segmentation and access controls to ensure only authorized agents see sensitive documents while still providing necessary context for tasks.
- Provide persistent memory for conversational agents to retain user state across sessions
- Supply segmented project context to multiple LLMs or assistants via MCP connectors
- Automatically refresh and surface up-to-date documents, notes, or telemetry as agent context
- Reduce prompt engineering by centralizing and serving relevant context to downstream models
- Integrate with multi-agent workflows to share and isolate context between agents