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

llm_generate

Read-only

Generate text using open-source LLM models hosted on Groq (ultra-fast) or HuggingFace Inference (serverless). No API key required — the server provides its own keys. Supported models: Qwen3 32B, Gemma 4 27B, Gemma 3 27B, Llama 3.3 70B, Llama 4 Scout, DeepSeek R1, Mistral Small 24B, and more. Use list_llm_models to see the full catalog. Rate-limited to prevent abuse.

Input Schema

TableJSON Schema

Name	Required	Description
`model`	No	Model ID (default: "openai/gpt-oss-20b"). Server-keyed whitelist only — Groq: openai/gpt-oss-20b, openai/gpt-oss-120b, qwen/qwen3.6-27b; HuggingFace: Qwen/Qwen3-32B, meta-llama/Llama-3.3-70B-Instruct, deepseek-ai/DeepSeek-R1, google/gemma-3-27b-it, and more. Other ids from list_llm_models are BYOK-only and will be rejected.
`prompt`	Yes	The user prompt / instruction to send to the model
`system`	No	Optional system prompt to set context or persona
`max_tokens`	No	Maximum tokens to generate (default: 2048, max: 4096)
`temperature`	No	Sampling temperature 0.0–1.5 (default: 0.7)

Output Schema

TableJSON Schema

Name	Required	Description	Default
`model`	No
`usage`	No
`content`	No
`provider`	No
`latency_ms`	No

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds rate limiting and server-provided keys, which are beyond annotations. No contradiction.

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?

Three concise sentences covering purpose, models, and rate limit. No fluff, but the model list is slightly verbose; could reference list_llm_models more directly. Still efficient.

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 output schema exists, description covers key aspects: purpose, providers, no API key, rate limiting, and a sibling reference. Sufficient for a generation tool.

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 description adds little extra beyond the schema. It does mention using list_llm_models for the full catalog, but that's minor extra value. 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?

Description clearly states 'Generate text using open-source LLM models' with specific providers (Groq, HuggingFace) and distinguishes from list_llm_models by instructing to use that sibling for the full catalog.

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?

Explicitly points to list_llm_models for full model list and mentions providers and rate limit, giving context. Does not explicitly state when not to use, but purpose is clear enough among siblings.

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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