Derive MCP

get_funding_rate_history

Retrieve historical funding rate data for perpetual contracts with timestamps and rates to analyze market trends and funding costs over time.

Instructions

Get historical funding rate data for perpetual contracts with timestamps and rates.

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_funding_rate_history"
  }
}

Input Schema

Name	Required	Description
`instrument_name`	No	Perpetual instrument (e.g., BTC-PERP, ETH-PERP) - optional to get all
`currency`	No	Filter by currency (optional)
`from_timestamp`	No	Earliest timestamp in milliseconds
`to_timestamp`	No	Latest timestamp in milliseconds
`page`	No	Page number (default 1)
`page_size`	No	Results per page (default 100, max 1000)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jan 7, 2026

TDQS

C2.9/5.0

Behavior2/5

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

With no annotations, the description carries full burden but only states what data is retrieved without disclosing behavioral traits like pagination behavior (implied by page/page_size parameters), rate limits, authentication needs, or error handling. It mentions 'historical' but lacks details on data freshness or availability constraints.

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

Conciseness5/5

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

The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part ('Get historical funding rate data for perpetual contracts with timestamps and rates') directly contributes to understanding the tool's function.

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

Completeness2/5

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

Given no annotations and no output schema, the description is incomplete for a tool with 6 parameters and financial data complexity. It lacks details on return format, error cases, or operational constraints, leaving significant gaps for an AI agent to use it effectively.

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%, so the schema fully documents all 6 parameters. The description adds no parameter-specific semantics beyond implying the tool handles perpetual contracts, which aligns with the instrument_name parameter. Baseline 3 is appropriate as the schema does the heavy lifting.

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

Purpose4/5

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

The description clearly states the action ('Get historical funding rate data') and resource ('for perpetual contracts'), specifying it includes timestamps and rates. It distinguishes from siblings like get_funding_history (likely broader) by focusing specifically on funding rates, though it doesn't explicitly contrast with all siblings.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives like get_funding_history or get_trade_history is provided. The description implies usage for historical funding rate data but offers no context about prerequisites, typical use cases, or exclusions.

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