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Glama
alekskram

dydx-agent-gateway

Historical Funding

historical_funding
Read-only

Retrieve realized hourly funding rate history for any market, shown oldest to newest with annualized percentage. Track long-to-short payment trends over up to 500 data points.

Instructions

Raw 1h funding rate history of a market (as realized, not the next-rate preview in market_detail): each point is the rate actually paid that hour; annualized_pct = rate x 24 x 365 for scale. Default 168 points = 7 days. Points are ordered oldest -> newest (candles convention); limit capped at 500. Positive = longs paid shorts. An unknown or delisted ticker raises an error (MCP isError) — call list_markets for the valid set (format: 'ETH-USD'). Example: historical_funding(ticker="ETH-USD", limit=168)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
tickerYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.3.0

TDQS

A5/5.0
Behavior5/5

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

Beyond the readOnlyHint/openWorldHint annotations, the description discloses important behaviors: points are ordered oldest to newest, limit is capped at 500, positive values mean longs paid shorts, and unknown/delisted tickers raise an MCP error. No annotations are contradicted.

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 dense but every sentence adds information: semantics, scale formula, default, ordering, sign convention, error handling, and a call example. It is front-loaded with the core purpose and uses the remaining sentences efficiently.

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?

Given the output schema exists, return values do not need to be described. The description covers the input format, defaults, error behavior, sign convention, ordering, and relationship to sibling tools, making it complete enough for an agent to invoke correctly without additional lookups.

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

Parameters5/5

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

Schema parameter coverage is 0%, so the description fully carries the burden. It explains the ticker format with a concrete example ('ETH-USD'), states the default limit of 168, and caps it at 500. Both parameters are meaningfully documented in prose.

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 opens with a specific verb and resource: 'Raw 1h funding rate history of a market'. It further distinguishes itself from the sibling market_detail by clarifying it is the realized rate, not the next-rate preview. An agent can tell exactly what this tool does and how it differs from nearby tools.

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

Usage Guidelines5/5

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

The description gives explicit usage context: it is for realized hourly funding history, not the market_detail preview, and it directs users to call list_markets for valid tickers. It also explains ordering convention, default window, and limit cap, so an agent knows when and how to use the tool correctly.

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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