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sqli_blind_boolean

Extract data from databases using blind SQL injection with binary search character enumeration. This tool sends HTTP requests to determine true/false conditions and returns extracted values efficiently.

Instructions

Boolean-based blind SQLi with binary search character enumeration. Uses ASCII(SUBSTRING(...))>N technique with binary search for efficiency. Determines true/false by comparing response lengths. Returns extracted_value, characters_found, requests_sent. Side effects: Read-only. Sends ~8 requests per character (binary search on ASCII 32-126).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesFull URL with injectable parameter
parameterYesVulnerable parameter name
queryNoSQL sub-query to extract, e.g. 'database()' or '(SELECT password FROM users LIMIT 1)'
max_lengthNoMaximum string length to extract
Behavior4/5

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

With no annotations provided, the description carries full burden and does well by disclosing key behaviors: 'Read-only' (safety profile), 'Sends ~8 requests per character' (rate/volume impact), and 'Determines true/false by comparing response lengths' (mechanism). It also describes output format ('Returns extracted_value, characters_found, requests_sent') and efficiency characteristics. Missing details about error handling or specific side effects beyond request count.

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?

Extremely concise and well-structured: first sentence defines purpose and technique, second explains mechanism, third describes outputs, fourth covers side effects and efficiency. Every sentence earns its place with zero wasted words, and critical information is front-loaded.

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 complex security testing tool with no annotations and no output schema, the description provides substantial context: purpose, technique, mechanism, outputs, side effects, and efficiency characteristics. It doesn't explain the binary search algorithm in detail or provide examples, but covers the essential operational context well given the complexity.

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 parameters are well-documented in the schema. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain how 'query' interacts with the binary search technique). Baseline 3 is appropriate when schema does the heavy lifting.

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 clearly states the tool performs 'Boolean-based blind SQLi with binary search character enumeration', specifying both the technique (SQL injection) and method (binary search). It distinguishes from siblings like 'sqli_blind_time' (time-based) and 'sqli_union_extract' (union-based) by explicitly mentioning its boolean-based approach with response length comparison.

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 description implies usage context through 'Boolean-based blind SQLi' and mentions efficiency ('binary search for efficiency'), but doesn't explicitly state when to use this tool versus alternatives like 'sqli_blind_time' or 'sqli_union_extract'. It provides technical context but lacks explicit comparative guidance.

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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