Calibrated AI skill routing via orbital mechanics — picks the right expertise for every query.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"io-github-luuow-meridian-skills": {
"args": [
"-y",
"meridian-orbital"
],
"command": "npx"
}
}
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npx -y 'meridian-orbital' 2>&1 | head -1 && echo "✓ Server started successfully"
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Dynamic task routing via orbital mechanics. Domain-agnostic — candidates can be tools, prompts, documents, products, or any routable entity.
Per request, an LLM (Llama-3.3-70B via GitHub Models' free tier) emits N candidate routing entries. A deterministic classifier extracts a 9-scalar physics signature from each candidate's content alone — no curated lookup: mass (log-scaled body length × keyword count), scope, independence, cross_domain affinity (token-domain entropy across three star systems), fragmentation, drag, dep_ratio (max sibling Jaccard), lagrange_potential, coherence_time (g⁽¹⁾-style autocorrelation over the candidate's token stream — added in 3.1.0), plus orbital and optical parameters (semi-major axis, eccentricity, inclination, period, perihelion, aphelion, mean anomaly; wavelength, polarization, amplitude, phase). Six per-class scoring rules assign a celestial body class by argmax: planet, moon, trojan, asteroid, comet, or irregular.
The class-scoring rules:
score_planet = min(mass, scope, independence)^1.5
score_moon = 2 · max(0, ½ - independence) · 𝟙[parent] · (1 - mass/2)
score_trojan = dep_ratio · 𝟙[parent] · (1 - fragmentation)
score_asteroid = 2.5 · max(0, 0.55 - mass) · scope · independence
score_comet = drag · cross_domain · (1 - dep_ratio)
score_irregular = 0.85 · cross_domain · fragmentation
class(p) = argmax_c score_c(p)
Output: a deterministically ranked list with route_score, full classification, and decision rule per candidate. Wire format: Model Context Protocol over stdio or Streamable HTTP. Stdio shim is ~5 KB. Tested with Claude Code, Cursor, Windsurf, Goose, Continue, Grok custom connectors, ChatGPT custom MCPs, and Claude.ai connectors.
3.0 — renamed from
meridian-skills-mcp. The classifier was always domain-agnostic; the "skills" framing biased the LLM prompt toward AI-agent capabilities. v3 drops that framing across the prompt, code, branding, and npm name. Migration:npm i -g meridian-orbital(the old package is deprecated; both binaries are still namedmeridian-mcp/meridian-mcp-httpso client configs keep working). The hosted HTTP MCP atmcp.ask-meridian.uk/mcpcontinues to work — URL unchanged.
npm install -g meridian-orbital
claude mcp add meridian meridian-mcp
Same install works in Cursor, Windsurf, Goose, Continue, and any MCP client that speaks stdio.
You'll need a GitHub personal access token with the Models: read permission (free tier). Generate one at https://github.com/settings/personal-access-tokens/new and export it:
export MERIDIAN_GITHUB_TOKEN=github_pat_...
(The MCP also picks up plain GITHUB_TOKEN if you have one already in your environment.)
A hosted Streamable-HTTP variant lives at https://mcp.ask-meridian.uk/mcp with full OAuth 2.1 + PKCE so it slots into any host that requires a connector URL — Grok's custom MCP connectors, ChatGPT custom MCPs, Claude.ai connectors. No npm install, no PAT entry from your side, no infra.
In Grok's "Add custom connector" dialog, paste these:
| Field | Value |
|---|---|
| Server URL | https://mcp.ask-meridian.uk/mcp |
| Authorization endpoint | https://mcp.ask-meridian.uk/authorize |
| Token endpoint | https://mcp.ask-meridian.uk/token |
| Client ID | grok |
| Client secret | (empty) |
| Token auth method | none (PKCE only) |
| Scopes | route_task |
When you click "Authorize" in Grok, it opens /authorize — a one-click confirmation page (no PAT pasting, no GitHub jargon). Infer