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@blockchainacademics/mcp

@blockchainacademics/mcp

Official
by blockchainacademics
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

get_exchange_flows

Analyze net inflows and outflows from centralized exchanges to detect smart-money signals and capital flows.

Instructions

Net in/out from CEXs — smart-money signal. Pro tier.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "symbol": "<symbol>"
    },
    "name": "get_exchange_flows"
  }
}

Input Schema

NameRequiredDescriptionDefault
symbolYes
windowNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.1

TDQS

C2.4/5.0
Behavior2/5

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

With no annotations provided, the description is the sole source for behavioral traits. It only states the data type ('Net in/out from CEXs') and a 'smart-money signal' tag, but does not disclose data freshness, update frequency, whether the operation is read-only, or any side effects.

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

Conciseness3/5

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

The description is extremely concise (one sentence), but it sacrifices necessary detail. While it avoids fluff, it fails to convey critical information about parameters and usage, making it under-specified rather than efficiently succinct.

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 the tool has two parameters, no output schema, and no annotation context, the description lacks completeness. It does not explain what the tool returns, how to interpret the output, or how the parameters affect results, leaving an AI agent with insufficient information to invoke the tool correctly.

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

Parameters1/5

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

Schema description coverage is 0% and the description adds no meaning to the two parameters ('symbol' and 'window'). For example, 'window' as a string is ambiguous; the description should clarify acceptable values (e.g., timeframes) but does not.

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 identifies the resource ('Net in/out from CEXs') and frames it as a 'smart-money signal' with a 'Pro tier' qualifier, making the tool's purpose reasonably clear. However, it does not explicitly differentiate it from sibling tools that also deal with exchange flows or signals.

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 provides no guidance on when to use this tool versus alternatives, nor does it mention prerequisites or exclusions. It only notes it is 'Pro tier,' implying a subscription requirement, but fails to give actionable usage context.

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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