OnchainAI vs Physbound — MCP Server Comparison | AllMCPs
Side-by-Side Model Context Protocol Comparison
OnchainAI vs Physbound
In-depth architectural comparison of the OnchainAI and Physbound MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.
At a Glance & Executive Verdict
OnchainAI
Monitoring · Remote HTTP/SSE
Quality: 56/100 (Good) | Auth: No auth required
Physbound
Monitoring · Local stdio
Quality: 52/100 (Good) | Auth: No auth required
Verdict Summary: Choose OnchainAI if you need specialized Monitoring tools running via a hosted cloud SSE transport. Choose Physbound if your workspace requires Monitoring integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
Choose OnchainAI when:
You need dedicated capabilities in the Monitoring domain.
You prefer remote streaming HTTP/SSE transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
OnchainAI is categorized under Monitoring and uses a remote streaming HTTP/SSE transport. In contrast, Physbound belongs to Monitoring using local stdio subprocess. Select OnchainAI when you need capabilities focused on monitoring and Physbound when you require tools for monitoring.
Side-by-side comparison of 2–4 tools on trust, risk, chains, pricing
get_price_history
Probe history and catalog x402 trends (metadata)
export_toolkit
Export a JSON + markdown install kit by slugs or category
recommend_verified_tool
Pick one verified/live tool for an intent with rejection reasons
gap_audit
Decompose an intent and report catalog coverage gaps
check_endpoint_health
Live endpoint probe + 30-day uptime for a listed x402 tool (HTTP 402 handshake)
Physbound Tools (6)
rf_link_budget
Computes a complete RF link budget using the Friis transmission equation and validates antenna gains against physical limits.
shannon_hartley
Computes Shannon-Hartley channel capacity `C = B log2(1 + SNR)` and validates throughput claims.
noise_floor
Computes thermal noise power `N = k_B T B`, cascades noise figures through multi-stage receivers with the Friis noise formula, and calculates receiver sensitivity.
radar_range
Computes the monostatic radar range equation `R_max = [P_t G^2 lambda^2 sigma / ((4pi)^3 S_min L)]^(1/4)` and validates detection-range claims.
antenna_gain
Analyses a single antenna from its diameter or physical area: gain limits, beamwidth, and far-field distance, with optional validation of a claimed gain.
radar_ambiguity
Computes pulse-Doppler ambiguity limits for a given carrier frequency and PRF.