pulsemcp Safe content atomic mcp-remote

MetriLLM

Runs comprehensive benchmarks against local LLM models via Ollama or LM Studio, measuring tokens per second, time to first token, memory usage, and quality across reasoning, coding, math, and multilingual categories. Produces a global score (0-100) combining hardware fit and quality metrics, with a verdict ranging from Excellent to Not Recommended. Results can be shared to a public leaderboard at metrillm.dev for cross-hardware comparison.

Cognium trust score

50%

Tier

Unverified

Composite of vulnerability cleanliness, spec conformance, provenance, stability, and usage signals — scanned and weighted by Cognium. Human and agent signals are tracked separately.

View full trust & usage report →

Metadata

Version: 1.0.0
Skill type: atomic
Execution layer: mcp-remote
Category: iot-hardware
Source: PulseMCP
Repository: github.com/metrillm/metrillm/tree/HEAD/mcp
Author type: human
Updated: 2026-04-29

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MCP

Resolve MetriLLM from your agent

Streamable HTTP transport at https://api.skillsregistry.net/mcp. No auth for read tools. Discovery: .well-known/mcp.json.

One command in your shell — Claude Code wires it up and verifies the connection. Run /mcp in any session to confirm.

claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp

Swap --scope user for --scope project to commit it to .mcp.json.

Your client Same endpoint everywhere; only the config key differs. The full list is in the descriptor.

Claude Desktop · Settings → Connectors → Add custom connector

https://api.skillsregistry.net/mcp

Paste the URL as the connector endpoint. No authentication.

ChatGPT · Settings → Connectors → Create (developer mode) → MCP server URL

https://api.skillsregistry.net/mcp

Authentication: none. Works in chat and deep research — the server exposes the connector search and fetch tools.

Codex CLI · ~/.codex/config.toml

[mcp_servers.skillsregistry]
url = "https://api.skillsregistry.net/mcp"