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bca-mcp

bca-mcp

by blockchainacademics
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_tokenomics

Retrieve token supply, emission schedule, vesting details, unlock cliffs, and circulating percentage from a single source.

Instructions

Supply, emission, vesting, unlock cliffs, circulating %. Pro tier. Single source replacing spreadsheet scraping.

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": {
      "entity_slug": "<entity_slug>"
    },
    "name": "get_tokenomics"
  }
}

Input Schema

NameRequiredDescriptionDefault
entity_slugYesEntity slug.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.1

TDQS

C2.6/5.0
Behavior2/5

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

No annotations are provided. The description does not disclose behavioral traits such as authentication requirements, rate limits, or whether it is read-only. As a 'getter', it implies no side effects, but this is not explicitly stated.

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 very short (three fragments) and to the point, but it lacks structured sentences or clear separation of concerns. It is efficient but could be more readable.

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?

For a simple tool with one parameter and no output schema, the description should at least indicate what the return data looks like or how to interpret the results. It does not provide sufficient context for an agent to fully understand the tool's output.

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

Parameters2/5

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

The input schema describes the sole parameter 'entity_slug' as 'Entity slug.' The tool description adds no further meaning or example values, so the parameter understanding relies entirely on the schema's minimal definition.

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 lists specific tokenomics aspects (supply, emission, vesting, etc.) and states it's a 'single source replacing spreadsheet scraping', clearly indicating the tool retrieves comprehensive tokenomics data for an entity.

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?

No explicit guidance on when to use this tool compared to siblings like 'generate_tokenomics_model' or other data retrieval tools. The description does not mention prerequisites or alternatives.

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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