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Sandbox Code Auditor MCP

by Whambammy
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sql_ast_sqli_taint_analyzer

Parses SQL query ASTs to verify parameterized binding and flag raw string concatenations that cause second-order SQL injection vulnerabilities.

Instructions

Parses SQL query ASTs to verify parameterized binding, flagging raw string concatenations that lead to second-order SQL injection vulnerabilities. (0.040 USDC on Base L2)

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": {
      "payload": "<payload>"
    },
    "name": "sql_ast_sqli_taint_analyzer"
  }
}

Input Schema

NameRequiredDescriptionDefault
payloadYesInput parameters or JSON string payload for the tool execution
paymentSignatureNoBase L2 USDC micropayment signature or transaction hash for x402 settlement

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations, the description carries the full transparency burden. It reasonably conveys a read-only analysis behavior through 'parses', 'verify', and 'flagging', and it discloses the execution cost. It does not, however, describe failure behavior, input expectations, or whether it modifies anything.

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, focused sentence that front-loads the core behavior and appends only the relevant cost. There is no repetition of the tool name and no filler.

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 complex analyzer with no output schema and no annotations, an agent still lacks critical invocation details: what exactly the payload should contain (SQL text vs AST JSON), how to represent the query, and what the output report looks like. The description explains the high-level purpose well but not enough to confidently construct a valid call.

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%, so baseline 3 applies. The parameter descriptions are generic boilerplate, and the tool description only broadly hints that the payload relates to SQL queries and ASTs. It does not specify the exact payload structure, but the schema already provides some description for both parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific operation ('Parses SQL query ASTs'), the intended goal ('verify parameterized binding'), and the vulnerability it targets ('raw string concatenations that lead to second-order SQL injection'). This clearly distinguishes it from generic validation or sanitization siblings like validate_code_syntax and sanitize_sql_query.

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

Usage Guidelines3/5

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

The intended use is implied by the function statement: use this when you need to verify SQL parameterized binding via AST analysis. However, there are no explicit conditions, exclusions, or references to alternatives such as sanitize_sql_query, so an agent is left to infer when this tool should be preferred.

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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