Toolport vs TrackMCP: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Toolport and TrackMCP — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Toolport

Free

Free open-source local MCP gateway. Set up each server once and share it across Claude, Cursor, VS Code, Codex, Windsurf.

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.

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.

Freemium

Analytics for MCP servers — see which AI clients connect, which tools they call, whether the work completes and what to fix.

One-line install: Drop the @trackmcp/sdk into an existing TypeScript or Python MCP server with no manual event tagging
Client breakdown: See the share of traffic coming from Claude, Cursor, ChatGPT and custom agents
Tool analytics: Per-tool call volume, adoption, latency percentiles and health status ranked in one table
Workflow paths: Follow sessions from first request to result and see exactly where they stop
Outcome tracking: Completion rates, sessions that reached a tool and returning clients within seven days
Hidden-error detection: Flags calls that report 200 OK while returning isError, with retry counts and a suggested fix
Real-time dashboard: Events appear as they happen across production and staging environments
Alerts: Slack and webhook notifications when a tool starts failing or a workflow degrades

An MCP server author finds out which of their tools agents actually call and which have never been used
A team diagnoses why a checkout workflow stops at 38% instead of completing, by replaying the session path
A maintainer catches a tool failing 94% of calls behind a 200 OK response that logs never surfaced
A product team measures whether new clients keep coming back within seven days of first connecting
An engineer compares latency and error rates across production and staging before shipping a schema change
A company decides which MCP tools to invest in by ranking them on adoption rather than guesswork