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bca-mcp

bca-mcp

by blockchainacademics
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

get_compute_pricing

Retrieve cloud GPU pricing from Akash, Render, and IO.net. Filter by GPU type like A100 or H100 to find cost for specific compute resources.

Instructions

Akash, Render, IO.net pricing per GPU type.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_compute_pricing"
  }
}

Input Schema

NameRequiredDescriptionDefault
gpuNoGPU type filter (e.g. 'A100', 'H100').

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.1

TDQS

B3.1/5.0
Behavior2/5

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

No annotations provided, and the description does not disclose behavioral traits such as whether the tool requires authentication, has rate limits, or aggregates data from multiple sources. The simple description implies a read operation but lacks detail.

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 a single, front-loaded sentence with no extraneous information. Every word is essential.

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 simple tool with one optional parameter and no output schema, the description provides the core purpose but misses a hint about the return format (e.g., 'returns a list of prices'). Adequate but not complete.

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 coverage is 100% (the only parameter is described). The description adds context by mentioning 'per GPU type', which ties the param to the tool's output, but it does not clarify behavior when the parameter is null (default). No additional meaning beyond schema.

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 specifies the tool returns pricing per GPU type for three platforms (Akash, Render, IO.net), which clearly identifies the resource and scope. It distinguishes from the many sibling tools that cover unrelated topics (e.g., defi, sentiment).

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 guidance on when to use this tool versus alternatives. While no sibling tool directly competes, context about when to query pricing (e.g., for cost comparison) is absent.

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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