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Carbon Cashmere MCP Server

Carbon Cashmere MCP Server

Official
by CarbonCashmere
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

get_v1_subtensor_derivatives_registration_pulse

Tracks the number of new neurons joining Bittensor subnets per hour, providing insight into subnet growth or decay and registration-burn pressure.

Instructions

Bittensor NeuronRegistered count per hour. Tracks new neurons joining subnets — useful for detecting subnet growth/decay and registration-burn pressure. AI agent API for subnet-health analysts. Informational research data — not investment advice. Price: $0.03. Category: general.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_v1_subtensor_derivatives_registration_pulse"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.2

TDQS

A3.6/5.0
Behavior2/5

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

No annotations are provided, and the description does not disclose behavioral traits such as rate limits, authentication requirements, data freshness, or pagination behavior.

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 description is reasonably concise with four sentences, front-loading the core function. Minor fluff like price and category could be omitted but does not harm clarity.

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

Completeness3/5

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

Given no parameters and no output schema, the description adequately explains what the tool returns, but lacks expectations about output format or pagination. Adequate for a simple tool.

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

Parameters4/5

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

Input schema has no parameters, so description does not need to add parameter details. Schema coverage is 100% by default, meeting the baseline for no-parameter tools.

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?

The description clearly states it provides 'Bittensor NeuronRegistered count per hour' and explains utility for subnet growth analysis. It distinguishes itself from sibling tools by specifying a unique metric.

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

Usage Guidelines3/5

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

The description mentions 'AI agent API for subnet-health analysts' implying usage context, but does not specify when to use this tool versus alternatives or provide when-not guidance.

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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