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Syrin - Static Contract Analysis for MCP Servers vs UCP Playground

Analyzes MCP contracts statically, while UCP Playground runs live integration tests

Side by side

What it is: Syrin - Static Contract Analysis for MCP Servers:Syrin is a Python library that adds budget enforcement, memory management, sandboxed code execution and guardrails to LLM agents.; UCP Playground:UCP Playground lets you debug, test, and benchmark AI shopping agents against live UCP-enabled stores.
Best for: Syrin - Static Contract Analysis for MCP Servers:Budget-controlled, safe multi-agent AI systems; UCP Playground:Debugging and benchmarking agentic commerce integrations
Who it’s for: Syrin - Static Contract Analysis for MCP Servers:Python developers building production AI agents.; UCP Playground:Store builders and AI agent developers integrating with commerce platforms
Pricing: Syrin - Static Contract Analysis for MCP Servers:Open source; UCP Playground:Free
Open source: Syrin - Static Contract Analysis for MCP Servers:Yes, 48 GitHub stars; UCP Playground:—
Works with: Syrin - Static Contract Analysis for MCP Servers:OpenAI, Anthropic, Google, Ollama, Datadog, PagerDuty; UCP Playground:Shopify, WooCommerce, MCP, REST, A2A, Embedded Checkout
DevHunt upvotes: Syrin - Static Contract Analysis for MCP Servers:24; UCP Playground:2
Launched on DevHunt: Syrin - Static Contract Analysis for MCP Servers:Feb 2026; UCP Playground:Mar 2026

First-class budget enforcement. Set dollar limits per agent; stop, warn or switch models when the budget is exceeded.
Budget-aware persistent memory. Four memory types with decay, import-rank and token-cost awareness, persisting across sessions.
Isolated sandbox execution. Run LLM-generated Python, Bash or JavaScript in subprocesses with timeouts and no shared state.
72+ lifecycle hooks. Typed events fire on every LLM request, tool call, memory read, sandbox exec, etc., for observability.
Built-in guardrails. PII redaction, prompt-injection detection, content filtering, fact verification and output length limits.
Multi-agent orchestration. Swarm topologies and recursive sub-agent spawning share budget, memory and observability.
Agent identity & signing. Each agent has a cryptographic Ed25519 identity; messages are signed to prevent impersonation.
Token-Oriented Object Notation (TOON). Compact schema format reduces token usage on tool calls by ~40%.

Debug console. Full request/response logging with timing, JSON inspection and transport-level tracing across MCP, REST and Embedded protocols.
Schema quality analyzer. Scores tool schemas A-F and catches missing definitions, required fields and untyped parameters before agents break.
Multi-model benchmarking. Run up to 5 LLMs side-by-side on the same store and prompt, comparing token usage, call count, duration and checkout success.
Session replay. Nanosecond-precision replay of every model call and tool invocation, viewable as chat or terminal log.
Store audit engine. Automated 4-phase UCP capability scan that scores performance, data quality and capability coverage.
OAuth flow inspector. Visualizes authorization code and PKCE flows, tracks token lifecycle, scopes and refresh state.
Embeddable replays. Share debug sessions via public URLs or embed as iframes for documentation and bug reports.
Transport fallback debugging. Shows MCP-to-REST fallback in real time, indicating which calls succeeded via MCP and which fell back.

Based on each tool's website and DevHunt data. Details may change; check the official sites.