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IAZENT

HuntX

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by IAZENT

sqli_scan

Probe query and body parameters for SQL injection using error-based and boolean-blind payloads. Flags confirmed matches on database error signatures and likely issues on response differences for review.

Instructions

Probe a request's query/body parameters for SQL injection.

Tries a small, high-signal payload set: error-based (single quote/paren breakouts, checked against real DB error signatures — flags "confirmed" only on an unambiguous match) and boolean-based blind (TRUE/FALSE condition pair, flags "likely" on a significant response diff — needs human review, a diff alone doesn't prove SQLi).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesFull URL to test.
bodyNoOptional JSON request body.
paramNoTest only this query param / body field by name. If omitted, tests every discovered injectable param.
methodYesHTTP method (GET, POST, etc.).
headersNoOptional request headers.
target_idYesIdentifier for the target.
scope_excludeNoScope denylist patterns.
scope_includeNoScope allowlist patterns.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior5/5

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

With no annotations provided, the description carries the full transparency burden and does so exceptionally well. It discloses the small high-signal payload set, distinguishes error-based 'confirmed' results from boolean-based 'likely' results, and explicitly warns that a response diff alone does not prove SQLi and requires human review.

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 front-loaded with the core purpose and then adds a compact, high-value explanation of detection methods and confidence levels. Every sentence earns its place, and there is no filler or repetition of schema details.

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?

Given the tool's complexity, the description sufficiently covers what the scanner does, how it classifies results, and the important limitation of boolean signals. The rich input schema and presence of an output schema mean the description does not need to explain return values or every parameter in detail.

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 baseline is 3. The description adds a small amount of context by framing the target as query/body parameters, but it does not need to repeat the detailed schema explanations already present for each parameter.

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 uses a specific verb and resource: "Probe a request's query/body parameters for SQL injection." This clearly distinguishes sqli_scan from sibling scan tools like ssrf_scan, ssti_scan, and xss_scan by naming the vulnerability class and the exact target surface.

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?

The context is clear: the tool is for probing query/body parameters for SQL injection, which tells an agent when to choose it. It does not explicitly name alternatives or exclusions, but the phrasing and sibling tool names make the intended use evident.

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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