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

prompt_template_fill

Read-onlyIdempotent

Fill a prompt template with variables. Supports {{variable}} syntax and {{#if key}}...{{/if}} conditional blocks. Returns the filled prompt and lists unfilled variables.

Input Schema

TableJSON Schema

Name	Required	Description
`strict`	No	Throw error if any variable is not provided (default: false)
`template`	Yes	Prompt template with {{variable}} placeholders
`variables`	No	Key-value pairs to fill (e.g. {"name":"Alice","role":"engineer"})

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	No
`total_vars`	No
`filled_variables`	No
`unfilled_variables`	No

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already establish read-only and idempotent behavior. The description adds useful behavioral details beyond those annotations: support for conditional blocks, returning the filled prompt, and listing unfilled variables. No contradiction with annotations.

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?

Two concise, front-loaded sentences each contribute necessary information: the core action, supported syntax, and return behavior. No wasted words.

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 rich schema, output schema, and annotations, the description fully covers the operation and its behavior. An agent can correctly invoke the tool without additional explicit 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 covers 100% of parameters with descriptions for template, variables, and strict. The description does not add further parameter-level meaning, so the baseline of 3 applies.

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 opens with a specific verb and resource ('Fill a prompt template with variables') and adds supported syntax and return behavior, making it clear and distinguishable from sibling prompt-related tools like few_shot_formatter or build_rag_prompt.

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?

Usage context is implied by the description: use when you have a prompt template with variables. However, there is no explicit when-to-use guidance or comparison to alternative tools, so the description only partially addresses this dimension.

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.

Resources

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