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smart_contract_verifier

Read-onlyIdempotent

Source code analysis, ABI fetching, and proxy validation ($0.02 USDC)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
addressYesTarget Base contract address to verify on Basescan

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesBasescan source code, ABI, and proxy details
successYesVerification status

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / address / description
      Added value: +"Target Base contract address to verify on Basescan"
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "properties": {
      +    "data": {
      +      "description": "Basescan source code, ABI, and proxy details",
      +      "type": "object"
      +    },
      +    "success": {
      +      "description": "Verification status",
      +      "type": "boolean"
      +    }
      +  },
      +  "required": [
      +    "success",
      +    "data"
      +  ],
      +  "type": "object"
      +}
  2. First observed

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the safety profile is covered. The description adds meaningful behavioral context beyond that: it performs multiple sub-operations (analysis, ABI fetching, proxy validation) and explicitly discloses a $0.02 USDC cost, which is not conveyed by the schema or annotations.

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 compact line that communicates the tool's main behaviors and its cost with zero redundancy. It is front-loaded with substantive capabilities rather than generic filler.

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

Completeness4/5

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

For a single-parameter, read-only tool with an output schema and three annotations covering safety and world-openness, the description is largely sufficient. It could add a little more context about input format expectations or verification prerequisites, but nothing critical for selection is missing.

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 parameter is already fully documented as a Base contract address to verify on Basescan. The tool description itself adds no further parameter-level detail, matching the baseline score.

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 three concrete functions—source code analysis, ABI fetching, and proxy validation—rather than just restating the tool name. It is differentiated from deployment-focused siblings like deploy_contract and deploy_arc_contract, though it lacks a single definitive verb such as 'verify'.

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 is given for when to use this tool versus alternatives. The description does not mention exclusions, prerequisites (e.g., contract must already be on Base), or sibling tools, leaving the agent to infer appropriate usage from the name and parameter 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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