IA-QA — 130+ QA & Dev Tools for AI Agents

prompt_injection_scan

Read-onlyIdempotent

Scan user input or prompts for common prompt injection patterns. Detects system prompt overrides, jailbreak attempts, role manipulation, encoding tricks, delimiter attacks (chat-template tags <|im_start|>/[INST]/<<SYS>> AND fake role headers imitating markdown or chat separators: "### System:", "--- SYSTEM ---", "---BEGIN SYSTEM OVERRIDE---", "--- SYSTEM:"), template/interpolation injection ({{...}}, ${...}), and context-exfiltration attempts ("repeat everything above"). A match inside quoted or fenced text (documentation citing a payload) is reported one severity level lower and marked quoted — never suppressed, since an LLM reading the document as data can still follow a quoted instruction. A quote preceded by a live imperative ("output the following: ...") keeps its full severity.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`input`	Yes	The user input or prompt to scan for injection patterns
`sensitivity`	No	Detection sensitivity (default: medium)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`notes`	No
`detections`	No
`risk_level`	No
`sensitivity`	No
`input_length`	No
`detections_count`	No
`quoted_detections`	No
`injection_detected`	No

TDQS

A4.3/5.0

Behavior5/5

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

Beyond the annotations (readOnly, idempotent, non-destructive), the description discloses nuanced behavior: how quoted/fenced content is handled (severity reduction, never suppressed) and that live imperatives keep full severity, providing extra transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is detailed and informative but somewhat lengthy with examples. It is well-structured as a single paragraph explaining purpose and behavior, though it could be slightly more concise.

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?

No output schema is provided, but the description implies the output includes severity levels and detection results. It covers the essential context for using the tool, though it stops short of explicit return format details.

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?

The input schema already provides descriptions for both parameters ('input' and 'sensitivity'). The description does not add further parameter-level detail, but since schema coverage is 100%, baseline 3 is appropriate.

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's function: scanning user input for prompt injection patterns, and lists specific categories (system prompt overrides, jailbreak attempts, etc.). It is unambiguous and distinguishes the tool from typical text processing tools.

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 indicates the tool is for security scanning of user-provided text, but does not explicitly state when not to use it or mention alternatives. However, the intent is clear enough for typical use cases.

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

All IA-QA — 130+ QA & Dev Tools for AI Agents tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0

Disambiguation2/5

Multiple tools overlap significantly: compare_models/llm_fit_finder/model_info/list_llm_models all compare models; similarity_score/embedding_similarity/run_semantic_tests all measure text similarity; detect_secrets/secret_scan/analyze_diff_bugs/pr_gatekeeper all scan for secrets. Descriptions attempt to differentiate, but the boundaries between many tools are unclear, making selection error-prone.

Naming Consistency4/5

The vast majority of tools follow a snake_case verb_noun pattern (validate_email, generate_uuid, parse_csv), making the set mostly predictable. A few notable deviations exist (pr_gatekeeper, llm_fit_finder, cot_analyzer, jira_to_test_suite, needle_haystack_generate) but they are the exception rather than the rule.

Tool Count1/5

With 149 tools, this set is far beyond the 50+ threshold for an extreme mismatch. Even as a general-purpose QA & Dev toolkit, the sheer number overwhelms and exceeds any reasonable scope, making discovery and selection impractical.

Completeness4/5

The toolkit covers an impressively broad range: text processing, LLM evaluation, security auditing, web checks, MCP validation, Jira/Confluence integration, and more. Minor gaps exist, such as missing delete/update for webhooks and Confluence pages, and no create/update for Jira issues, but these are workable around.

Resources

Need Help?