SigRank — AI Operator Benchmarking is an MCP server with 16 tools an agent can call, among them benchmark_me, compare_to_field, diagnose_cascade. It answers at https://signalaf.com/api/mcp over streamable HTTP, and it listed its tools to an anonymous client, so no sign-in was needed for that. Our probes reached it 6 of 6 times since 10 September 2026; the last check, on 19 October 2026, succeeded.
SigRank benchmark MCP: cascade metrics, leaderboard, operator profiles, simulation, diagnostics.
Description as the publisher wrote it.
| Tool | What it does |
|---|---|
benchmark_me | Answers 'How good am I?' — benchmarks your token cascade against the live field. Takes 4 token pillars (or a codename), computes your cascade, then compares against the live leaderboard: percentile, rank, distance from median, distance from Inputs: input, output, cache_read, cache_write, codename, window |
compare_to_field | Creates a 'YOU vs FIELD vs TOP 10% vs TOP 1%' comparison table for your cascade metrics. Takes 4 pillars or a codename, fetches the live leaderboard, and returns your metrics alongside field median, top quartile, top decile, and top percent Inputs: input, output, cache_read, cache_write, codename, window |
diagnose_cascade | Analyzes your token cascade and diagnoses where you're leaking efficiency. Takes 4 token pillars and produces a ranked list of efficiency leaks with severity (critical/warning/info), findings, recommendations, and estimated Υ impact. Checks Inputs: input, output, cache_read, cache_write, context |
field_anomaly | Finds unusual operators, metric relationships, and outliers in the live leaderboard — without user prompting. Returns: highest velocity among below-median leverage operators, only top-50 operator with near-zero cache write, largest 30-day y Inputs: window, context |
get_leaderboard | Read the current public SigRank operator leaderboard. Returns ranked operators with Yield, Leverage, class tier, and display name. Inputs: limit, window, context |
get_operator | Read one public operator profile by codename. Returns class tier, rank, percentile, Yield, Leverage, Velocity, and SNR. Inputs: codename, context |
get_sigrank_standard_record | Build a SigRank Standard v0.1-draft portable operator record from available token telemetry. Input and output are required; unavailable cache telemetry remains null. Computes only the five-metric portable core through @sigrank/cascade and d Inputs: input, output, cache_write, cache_read, provider, model |
operator_gap | Answers 'What specifically separates operator A from operator B?' — not just 'A has more Yield', but the primary cause, secondary cause, and offsetting weakness. Takes two codenames or two sets of pillars, computes both cascades, and decomp Inputs: a_codename, b_codename, a_input, a_output, a_cache_read, a_cache_write |
operator_signature | Computes a normalized operating signature from 4 token pillars or a codename. Returns a signature code, a legacy six-label signature_label, dominant trait, and closest comparable operators from the live board. The deprecated archetype field Inputs: input, output, cache_read, cache_write, codename, window |
rank_if | Answers 'What would it take to reach a target rank?' — takes your current 4 token pillars and a target percentile (e.g. 90 for top 10%), then simulates the smallest metric changes needed to reach that position. Returns: current rank/percent Inputs: input, output, cache_read, cache_write, target_percentile, window |
rank_paste | Calculate SigRank cascade metrics from four non-negative token counts without submitting data. Returns Yield, Leverage, Velocity, SNR, and 10xDEV. No data is persisted. Inputs: input, output, cache_read, cache_write, context |
rank_windows | Score up to 4 time windows (7d, 30d, 90d, all-time) in one call. Each window is scored independently with the full cascade (Υ, SNR, Leverage, Velocity, 10xDEV, class). Omit windows you don't have — partial input is allowed (1-4 windows). Do Inputs: 7d, 30d, 90d, all, context |
self_improve | Runs the full self-improvement cycle in one call: (1) computes your current cascade from 4 token pillars, (2) diagnoses efficiency leaks, (3) generates ranked improvement suggestions, (4) simulates the top suggestion, and (5) returns the co Inputs: input, output, cache_read, cache_write, context |
simulate_change | Prescriptive 'what if' tool — takes your current 4 token pillars and proposed changes, runs the cascade on both, returns the exact Υ Yield delta, class change, and per-metric diffs. Test proposed pillar changes and see the payoff before cha Inputs: input, output, cache_read, cache_write, changes, context |
suggest_improvements | Generates ranked, simulated improvement suggestions for your token cascade. Takes 4 token pillars, tests multiple strategies (increase cache reads, reduce input, increase output, optimize cache creation), simulates each, and returns them ra Inputs: input, output, cache_read, cache_write, context |
who_operates_like_me | Finds operators whose operating signature most resembles yours. Takes 4 pillars or a codename, computes your signature, then searches the live leaderboard for the nearest neighbors by signature distance. Returns: nearest operators, similari Inputs: input, output, cache_read, cache_write, codename, window |
Every block below points at https://signalaf.com/api/mcp. Pick your client and copy.
claude mcp add --transport http sigrank-ai-operator-benchmarking https://signalaf.com/api/mcp
{
"mcpServers": {
"sigrank-ai-operator-benchmarking": {
"command": "npx",
"args": ["-y", "mcp-remote", "https://signalaf.com/api/mcp"]
}
}
}{
"mcpServers": {
"sigrank-ai-operator-benchmarking": {
"url": "https://signalaf.com/api/mcp"
}
}
}{
"servers": {
"sigrank-ai-operator-benchmarking": {
"type": "http",
"url": "https://signalaf.com/api/mcp"
}
}
}{
"context_servers": {
"sigrank-ai-operator-benchmarking": {
"url": "https://signalaf.com/api/mcp"
}
}
}{
"mcpServers": {
"sigrank-ai-operator-benchmarking": {
"serverUrl": "https://signalaf.com/api/mcp"
}
}
}{
"mcpServers": {
"sigrank-ai-operator-benchmarking": {
"url": "https://signalaf.com/api/mcp"
}
}
}name: SigRank — AI Operator Benchmarking
version: 0.0.1
schema: v1
mcpServers:
- name: SigRank — AI Operator Benchmarking
type: streamable-http
url: https://signalaf.com/api/mcp{
"mcpServers": {
"sigrank-ai-operator-benchmarking": {
"httpUrl": "https://signalaf.com/api/mcp"
}
}
}{
"mcpServers": {
"sigrank-ai-operator-benchmarking": {
"type": "http",
"url": "https://signalaf.com/api/mcp"
}
}
}{
"mcpServers": {
"sigrank-ai-operator-benchmarking": {
"url": "https://signalaf.com/api/mcp"
}
}
}goose://extension?url=https%3A%2F%2Fsignalaf.com%2Fapi%2Fmcp&type=streamable_http&id=sigrank-ai-operator-benchmarking&name=SigRank%20%E2%80%94%20AI%20Operator%20Benchmarking
It answered our last check on 19 October 2026 in 883 ms. Over 6 checks since 10 September 2026 it answered 6. Status here comes from Neuronto's own probes, not from the publisher.
Yes. It is published by signalaf.com and served from signalaf.com, the vendor's own domain: https://signalaf.com/api/mcp.
16, as returned by the server's own tools/list on 11 September 2026: benchmark_me, compare_to_field, diagnose_cascade, field_anomaly, get_leaderboard, get_operator, get_sigrank_standard_record, operator_gap, and 8 more.
It answered our last check on 19 October 2026 in 883 ms. Over 6 checks since 10 September 2026 it answered 6.
Put the server under mcpServers in ~/.cursor/mcp.json with the URL https://signalaf.com/api/mcp. The exact JSON is above, with the same setup for Claude Code, Claude Desktop, VS Code, Windsurf, Zed, Gemini CLI and the rest.
It listed its tools to an anonymous client, so reading the tool list needs no key. Individual tools can still ask for credentials in their own inputs.
Publisher: com.signalaf. Endpoint host: signalaf.com. ARD identifier: urn:air:com.signalaf:mcp:sigrank.
First indexed 10 September 2026. The tools are exactly what the server returned; nothing on this page
is inferred from its name. Ranking in Neuronto search is never for sale.