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Glama

AgentData — crypto market data with a checkable record

by io.github.speteai

get_signal_history_7d

Recorded hourly history of a derived signal, most recent 7 days ($0.005 USDC). Cannot be reconstructed from public sources after the fact.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
signalYesWhich recorded signal, e.g. sentiment, volatility, funding-rates
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. Changed1 schema field changed
    • addedInput schema / properties / payment
      Added value: +{
+  "description": "Optional. 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.",
+  "type": "string"
+}
  2. Added

TDQS

A3.5/5.0
Behavior3/5

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

The description usefully discloses the hourly granularity, 7-day limit, USDC cost, and the fact that the data cannot be reconstructed from public sources afterward. It does not explain the return format, pagination, or the two-step payment/payment-requirements flow, though the payment flow is described in the input schema. With no annotations present, the description only partially carries the behavioral transparency burden.

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 short sentences that immediately state the core capability, time window, and cost. The final clause about non-reconstructability is a valuable differentiator, and no words are wasted.

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?

For a 2-parameter tool with full schema coverage, the description gives the essential duration and pricing context. However, because there is no output schema, the description does not clearly explain the response shape or expected data series, and it leaves sibling-selection inference to the agent.

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?

The input schema already provides 100% coverage, including signal examples and a detailed payment parameter description. The description adds little parameter-specific meaning beyond 'signal history', so the baseline 3 is appropriate.

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 names the resource: an hourly recorded derived-signal history for the most recent 7 days, and even adds the price. It distinguishes from 'public' data sources, but it does not explicitly differentiate from sibling tools like get_signal_history_30d or get_signal_history_full in the text itself. Overall, the core purpose is clear and actionable.

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?

Usage context is implied: use this when a recorded 7-day hourly signal history is needed and when public reconstruction is insufficient. However, the description provides no explicit when-to-use/when-not-to-use guidance and does not mention which sibling tool should be chosen for longer history windows.

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.

Resources