INDIVIDUAL MCP TOOL

list_tested_configs

List every tested GPU, model, quantization, and runner type in the PPB dataset.

poorpaul.devnone authenticationAvailability not checked

LIVE ENDPOINT

https://mcp.poorpaul.dev/mcp

No auth detected

Connect to this endpoint to inspect the live schema for list_tested_configs and invoke it with your own arguments.

{}

Inferred read-only · medium confidence · heuristic, not a guarantee.

No write-capable action terms were found; this is not proof that invocation has no side effects.

CONNECT WITH APPROVAL

Review this server and its permissions before adding it. Secret placeholders must be set locally.

Codex

~/.codex/config.toml

[mcp_servers.poor-paul-s-mcp]
url = "https://mcp.poorpaul.dev/mcp"
enabled = true

Claude Code

.mcp.json

{
  "mcpServers": {
    "poor-paul-s-mcp": {
      "type": "http",
      "url": "https://mcp.poorpaul.dev/mcp"
    }
  }
}

Claude Desktop

Settings → Connectors → Add custom connector

Name: poor-paul-s-mcp
Remote MCP URL: https://mcp.poorpaul.dev/mcp

Add this remote URL as a custom connector in Claude Desktop. Availability depends on the user plan and workspace policy.

Cursor

.cursor/mcp.json

{
  "mcpServers": {
    "poor-paul-s-mcp": {
      "url": "https://mcp.poorpaul.dev/mcp"
    }
  }
}

Visual Studio Code

.vscode/mcp.json

Add to Visual Studio Code

{
  "servers": {
    "poor-paul-s-mcp": {
      "type": "http",
      "url": "https://mcp.poorpaul.dev/mcp"
    }
  }
}

Generic MCP

Client-specific MCP configuration

{
  "name": "poor-paul-s-mcp",
  "transport": "streamable-http",
  "url": "https://mcp.poorpaul.dev/mcp"
}

MCP Inspector

Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.

query_ppb_results — Filter raw benchmark rows from PPB.
recommend_quantization — Recommend the best quantization for a given GPU VRAM budget and user count.
get_gpu_headroom — Sanity-check VRAM usage for a specific (gpu, quant, model, users) config.
compare_quants_quantitative — Compare quantitative benchmark scores across quantizations for a model.
get_combined_scores — Get both quantitative (speed/VRAM) and qualitative (accuracy/quality) scores for a single (gpu, model, quant) configuration in one call.
rank_by_priority — Rank all tested quantizations for a model by a composite score.
recommend_hardware — Recommend the best GPU for running a specific model.
explain_result — Explain why a benchmark result is what it is.