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

ssl_certificate_check

Read-only

Analyse the SSL/TLS certificate of any HTTPS host. Returns certificate subject, issuer, validity dates, days until expiry, protocol version, cipher suite, key exchange info, and an overall grade (A+, A, B, C, F). Detects expired, self-signed, and weak certificates. Use this to audit TLS posture before production deployment or during security reviews.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hostYesHostname to check (e.g. example.com). Do not include https:// prefix.
portNoPort number (default: 443)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
hostNo
gradeNo
cipherNo
issuerNo
issuesNo
subjectNo
protocolNo
valid_toNo
is_expiredNo
valid_fromNo
is_self_signedNo
days_until_expiryNo

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already mark readOnlyHint=true and destructiveHint=false, and the description further explains that it detects expired, self-signed, and weak certificates, and returns a grade. This adds useful behavioral context beyond the annotations, such as its network-based external host checking. 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.

Conciseness5/5

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

Two concise sentences that front-load the core function, then list key outputs and use cases. Every sentence adds value, and there's no redundancy with the schema or annotations.

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?

With an output schema present and clear annotations, the description need not restate return structures. It provides the essential behavioral and usage context, including what the tool checks, what it returns, and when to use it. This is complete for a tool of this complexity.

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%, with both host and port having clear descriptions. The description reinforces the host parameter by saying 'any HTTPS host' and implies the default port behavior indirectly, but adds no extra parameter details beyond the schema. 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 identifies the action (analyse SSL/TLS certificate) and resource (any HTTPS host), and enumerates specific return values (subject, issuer, validity, grade). This distinguishes it from sibling security tools like security_headers_check or web_security_audit, which target other aspects of web security.

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?

Provides explicit use cases: 'audit TLS posture before production deployment or during security reviews.' It doesn't explicitly contrast with sibling tools, but the focused scope makes the intended usage clear. It would be stronger with an explicit 'for checking HTTP headers use security_headers_check,' but the context is sufficient.

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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