Skip to main content
Glama

AgentData — crypto market data with a checkable record

by io.github.speteai

get_macro_onchain

Macro & on-chain context: stablecoin supply, BTC/ETH dominance, market cap, BTC network fundamentals, Fear & Greed, trending ($0.003 USDC)

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. 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

B3.4/5.0
Behavior3/5

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

There are no annotations, so the description itself carries the behavioral disclosure burden. It does reveal a fee/trending cost of $0.003 USDC, which is useful, and the schema separately discloses the payment flow and key handling. However, it does not clarify whether the operation is read-only, what side effects payments have, or how the returned data behaves.

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 a single sentence and front-loaded with the tool's purpose. It uses a terse list effectively, though the parenthetical 'trending ($0.003 USDC)' is ambiguous and could be clearer.

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 tool with no output schema and one optional payment parameter, the description lists the expected data categories well. Still, it lacks usage guidance, no explicit output shape, and leaves the meaning of 'trending ($0.003 USDC)' unresolved, especially against a large sibling set with overlapping macro/on-chain tools.

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 payment parameter is already fully documented in the input schema. The description adds the contextual cost figure, but it does not explain the parameter mechanics beyond what the schema provides. 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 identifies the resource as macro & on-chain context and enumerates its concrete content: stablecoin supply, BTC/ETH dominance, Fear & Greed, BTC network fundamentals. It avoids tautology, but it lacks an explicit verb and does not directly differentiate itself from overlapping siblings like get_stablecoin_health or get_market_overview.

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 content list implies when the tool is relevant, namely when an agent needs macro or on-chain context. However, it offers no explicit guidance about when to prefer this tool over siblings and provides no exclusions or alternative routing.

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

All AgentData — crypto market data with a checkable record toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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