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Glama

AgentData — crypto market data with a checkable record

get_arbitrage_spread_history

30 days of hourly cross-exchange spreads (MEXC/Binance/Bybit/OKX) ($0.015 USDC). Every venue publishes its own prices; nobody archives the spread between them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paymentNoOptional. Base64 x402 payment payload you signed yourself (ERC-3009 TransferWithAuthorization, USDC on Base). Call without it once to receive the payment requirements, sign those, then call again with this argument. This server forwards the payload and never holds a key.

Schema Changelog

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

  1. Added

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations provided, the description carries the full disclosure burden. It does disclose the $0.015 USDC cost and the unique data provenance, which is useful. However, it does not describe the return format, pagination, error behavior, or what happens when the payment parameter is omitted beyond what the schema already states.

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 two sentences with no filler. It front-loads the essential facts (time range, frequency, venues, cost) and then adds a meaningful uniqueness statement. Every part contributes value.

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

Completeness4/5

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

For a single-parameter, read-only data retrieval tool, the description is nearly complete: it covers the data content, time span, frequency, exchanges, cost, and why the data is valuable. The main gap is the absence of an output schema explanation, but the input schema already handles the only parameter, so an agent can still invoke the tool correctly.

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 single optional payment parameter is already fully documented. The description adds the cost amount and context about why this data is unique, but it does not add parameter-level meaning beyond the schema, so the baseline score of 3 applies.

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 identifies the resource: 30 days of hourly cross-exchange spreads across four named venues, with an explicit cost. It distinguishes itself from siblings like get_arbitrage_opportunities and get_crypto_prices by specifying historical spread data rather than current opportunities or single-venue prices.

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?

The description does not say when to use this tool versus alternatives, nor does it mention any exclusion criteria or competing sibling tools. The uniqueness statement ('nobody archives the spread between them') implies value, but it does not provide practical usage 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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TDQS

B3.2/5.0
Disambiguation3/5

Most tools target clearly distinct metrics, but the paid/free sample pairs (get_crypto_prices/try_crypto_prices, etc.) and overlapping summary tools (get_market_overview, get_market_pulse, get_overnight_risk_brief) create some selection ambiguity. Descriptions list components, so an agent can disambiguate with effort, but the boundaries between bundles and single-purpose tools are not always obvious.

Naming Consistency5/5

Tool names consistently follow a verb_noun pattern: get_ for data retrieval, try_ for free samples, and watch_condition for persistent monitoring. All names use lowercase snake_case with no mixed conventions, making the naming predictable and easy to navigate.

Tool Count2/5

34 tools is well above the comfortable range for a typical MCP server and feels heavy even for a broad crypto data domain. The count is inflated by paid/free sample duplicates and multiple bundle variants that could have been consolidated.

Completeness4/5

The surface covers a wide range of crypto data: prices, funding, sentiment, arbitrage, derivatives, on-chain metrics, signal history, and monitoring. Minor gaps exist—such as no obvious generic signal discovery tool or order-book/trade-level data—but the core domain of market data with checkable records is thoroughly served.