qubic-mcp

query_contract

Call registered smart contract functions on the Qubic network with automatic encoding and decoding. Provide contract name, function name, and input parameters to get human-readable results.

Instructions

Query a registered smart contract function with automatic binary encoding/decoding.

Use register_contract first to define the contract's functions and their field schemas. Then call this tool by contract name and function name.

Input values are provided as a JSON object with field names as keys. For example: {"gateId": 1}

The response is automatically decoded into human-readable named fields.

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": {
      "contract": "<contract>",
      "function": "<function>"
    },
    "name": "query_contract"
  }
}

Input Schema

Name	Required	Description
`input`	No	JSON object of named input parameters (e.g., {"gateId": 1}). Omit for functions with no input.
`contract`	Yes	Name of the registered contract
`function`	Yes	Name of the function to call

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.5.2Jul 31, 2026

TDQS

A4.1/5.0

Behavior4/5

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

Despite no annotations, the description discloses key behaviors: automatic binary encoding/decoding, input as JSON object with named fields, and response decoded to human-readable fields. This goes beyond the schema and helps the agent anticipate input/output transformations.

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?

Four short sentences efficiently cover purpose, prerequisite, input format, and output behavior without redundancy. Each sentence contributes value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the full workflow: registration prerequisite, invocation method, input format, and decoded output. It lacks an explicit comparison to query_smart_contract, but the registered-contract scope and automatic decoding are sufficient for 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?

Schema covers 100% of parameters with descriptions, and the description repeats the input format without adding new details. It reinforces the example but doesn't add meaning beyond what the schema already provides, so baseline 3.

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 states the tool 'Quer[ies] a registered smart contract function' with specific encoding/decoding behavior. It is distinct from raw queries by requiring registration, though it doesn't explicitly differentiate from the sibling query_smart_contract.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly instructs to 'Use register_contract first' and then call by contract/function name, providing a clear usage sequence. It does not list alternatives or when-not-to-use scenarios, so it misses explicit exclusions.

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