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

system_prompt_builder

Read-onlyIdempotent

Build a structured system prompt from components: role, task, constraints, output format, tone, language, and examples. Generates a production-ready system prompt with token estimate.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
roleYesRole/persona (e.g. "Senior QA Engineer", "JSON extraction assistant")
taskNoMain task or objective
toneNoCommunication tone
examplesNoBrief examples to include
languageNoResponse language (e.g. "French")
constraintsNoRules and constraints to follow
output_formatNoExpected output format description

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
sectionsNo
system_promptNo
token_estimateNo

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is well covered. The description adds valuable context by stating it generates a production-ready system prompt with a token estimate, which goes beyond the annotations. No contradiction is present.

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 exactly two sentences: the first states the action and components, the second states the output and token estimate. It is front-loaded with the verb 'Build' and contains no unnecessary words.

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?

Given the tool has 7 parameters, full schema descriptions, a rich annotation set, and an output schema, the description adequately covers the overall behavior. It mentions the key output (structured system prompt) and the token estimate, which is sufficient for an agent to select and invoke the tool. It does not explain edge cases or prerequisites, but those are not essential here.

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 has 100% description coverage, and the description merely lists the parameter names in prose. It does contextualize them as 'components' but adds no additional meaning beyond what the schema already provides. This meets the baseline for full schema coverage.

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 states the specific action 'Build a structured system prompt' and enumerates all component types (role, task, constraints, output format, tone, language, examples). This clearly distinguishes it from sibling prompt-related tools like build_rag_prompt or few_shot_formatter by emphasizing a structured, component-driven system prompt builder with a token estimate.

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 provides a clear context for use: when you need to assemble a system prompt from the listed components. However, it does not explicitly mention when not to use this tool or suggest alternatives among the sibling tools, so it stops short of a full 5.

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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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.

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