bestuser-router-mcp

get_best_operator

Read-only

Discover top AI power-users by sorting the SigRank leaderboard on yield, leverage, or 7 other metrics, filter by platform, and get plain-language behavioral insights.

Instructions

Returns the top N operators on the SigRank leaderboard with behavioral framing in power-user language. Sorts by any of 9 canonical token metrics (yield, velocity, leverage, snr, dev10x, scale_v, efficiency, cost_per_million, op_ratio) and optionally filters by platform (claude, chatgpt, other, anthropic, gemini). Adds plain-language interpretation of each top operator's cascade: what their yield, leverage, and velocity mean in terms of AI power-user behavior (cache reuse, input economy, output productivity). Use this when users ask 'who is the best AI user?' or 'who tops the SigRank leaderboard?' or 'who has the best leverage?' or 'cheapest tokens?' or 'show me the AI user leaderboard'. Intent: BEST_OPERATOR.

Input Schema

TableJSON Schema

Name	Required	Description
`n`	No	Number of top operators to return (default: 5, max: 20).
`metric`	No	Canonical token metric to sort by (default: yield). Each metric answers a different 'who is the best?' question: yield = best overall, velocity = most output per token, leverage = most context reuse, snr = cleanest signal, dev10x = most normalized, scale_v = largest scale, efficiency = most efficient overall, cost_per_million = cheapest tokens, op_ratio = best op ratio.
`platform`	No	Filter to a single platform (default: all). Options: all, claude, chatgpt, other, anthropic, gemini.

Output Schema

TableJSON Schema

Name	Required	Description
`cta`	No	Call-to-action per CTA hierarchy
`summary`	No	One-line headline
`shareable_url`	No
`top_operators`	No
`total_operators`	No

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedAug 27, 2026

changedInput schema / description

Previous value: -"Optional: how many top operators to return. Defaults to 5."New value: +"Optional params: n (top N, default 5), metric (sort metric, default yield), platform (filter, default all)."

addedInput schema / properties / metric

Added value: +{
+  "description": "Canonical token metric to sort by (default: yield). Each metric answers a different 'who is the best?' question: yield = best overall, velocity = most output per token, leverage = most context reuse, snr = cleanest signal, dev10x = most normalized, scale_v = largest scale, efficiency = most efficient overall, cost_per_million = cheapest tokens, op_ratio = best op ratio.",
+  "enum": [
+    "yield",
+    "velocity",
+    "leverage",
+    "snr",
+    "dev10x",
+    "scale_v",
+    "efficiency",
+    "cost_per_million",
+    "op_ratio"
+  ],
+  "type": "string"
+}

changedInput schema / properties / n / description

Previous value: -"Number of top operators to return (default: 5, max: 20). Returns the top N by yield."New value: +"Number of top operators to return (default: 5, max: 20)."

addedInput schema / properties / platform

Added value: +{
+  "description": "Filter to a single platform (default: all). Options: all, claude, chatgpt, other, anthropic, gemini.",
+  "enum": [
+    "all",
+    "claude",
+    "chatgpt",
+    "other",
+    "anthropic",
+    "gemini"
+  ],
+  "type": "string"
+}

First observedv0.1.1Jul 17, 2026

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and openWorldHint=true, covering the safety profile. The description adds meaningful behavioral context beyond the annotations: the tool does not just return raw rankings but adds 'plain-language interpretation of each top operator's cascade' in terms of cache reuse, input economy, and output productivity. No contradiction with annotations (a read operation). It omits edge-case behavior but the annotation coverage lowers the bar.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The core purpose is front-loaded in the first sentence. The trigger examples and intent label earn their place. The tradeoff is some redundancy: the nine metric names and platform list are duplicated from the schema, slightly lengthening the description without adding new information. Efficient overall, with minor bloat.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return values need no explanation. All three optional parameters are documented in the schema with defaults and constraints. The description covers what the tool returns, how it behaves (sort, filter, interpret), and when to invoke it. For a read-only leaderboard query of this complexity, nothing an agent needs to call it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the schema descriptions are already rich: n has min/max/default, metric includes per-metric semantics ('yield = best overall, velocity = most output per token...'), and platform lists all enum values with default. The tool description enumerates the metrics and platforms but adds no meaning beyond the schema. Baseline 3 applies since the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description opens with a specific verb+resource: 'Returns the top N operators on the SigRank leaderboard'. The scope is precisely defined (top N, sortable by 9 named metrics, optionally platform-filtered), and this clearly differentiates it from siblings like compare_operators (comparison), discover_peers (peer discovery), and compare_self (self-comparison). The embedded 'Intent: BEST_OPERATOR' label and example user queries reinforce what the tool is for.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit trigger phrases: 'who is the best AI user?', 'who tops the SigRank leaderboard?', 'who has the best leverage?', 'cheapest tokens?', 'show me the AI user leaderboard'. This gives strong when-to-use guidance. However, it does not state when not to use it or route to alternatives (e.g., compare_operators for direct two-user comparison), so it stops short of a full 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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