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

generate_hmac

Read-onlyIdempotent

Compute an HMAC signature for a message using a secret key. Supports SHA-256 (default), SHA-512, SHA-1, and MD5. Used for API request signing, webhook verification (GitHub, Stripe, Twilio), and JWT validation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
secretYesSecret key
messageYesMessage to sign
encodingNoOutput encoding (default: hex)
algorithmNoHash algorithm: sha256 (default), sha512, sha1, md5

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
hmacNo
encodingNo
algorithmNo
message_lengthNo

TDQS

A4.1/5.0
Behavior3/5

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 known. The description adds algorithm support and default behavior, but it does not disclose output formatting details (e.g., lowercase hex) or any security nuances such as avoiding MD5 in production. With annotations provided, this level of added context is adequate but not exceptional.

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 two sentences long, front-loads the core function, and provides practical context in the second sentence. Every word earns its place with no redundancy or filler.

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?

For a simple, pure, read-only cryptographic utility with a full input schema, output schema, and strong annotations, the description is complete. It covers purpose, supported algorithms, defaults, and real-world use cases, leaving no meaningful gap for an agent to invoke it correctly.

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 the schema already fully describes message, secret, encoding, and algorithm. The description repeats the algorithm choices and default but adds no new parameter-level meaning beyond what the schema provides, placing it at the baseline of 3.

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 starts with a specific action, 'Compute an HMAC signature for a message using a secret key,' clearly identifying the tool's resource and operation. It distinguishes itself from sibling tools like hash_text and base64_encode by explicitly naming HMAC and keyed signing, and it reinforces this with concrete use cases such as API signing and webhook verification.

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 lists explicit usage contexts: 'API request signing, webhook verification (GitHub, Stripe, Twilio), and JWT validation.' While it does not mention when not to use the tool or name alternatives, the stated use cases are clear enough for an agent to select this tool 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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