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cors_test

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Test a URL for CORS misconfigurations. Sends preflight (OPTIONS) requests with various Origin headers to detect: wildcard origins with credentials, origin reflection (echoing any origin), null origin acceptance, subdomain wildcard bypass, and missing Vary headers. Returns risk level (safe/low/medium/high/critical) plus per-origin results. "unknown" means nothing was actually tested — every origin either failed to connect or answered 5xx, so the target returned no CORS decision; never read it as "safe". A 4xx preflight IS a real result (the server refused it and a browser would fail closed).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesFull URL to test (e.g. https://api.example.com/endpoint)
originNoCustom Origin header to test (default: tests multiple origins automatically)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNo
errorNo
testsNo
warningNo
risk_levelNo
origins_testedNo
total_findingsNo
origins_reachableNo
origins_conclusiveNo
origins_inconclusiveNo

TDQS

A4.3/5.0
Behavior5/5

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

The description adds substantial behavioral nuance beyond the annotations: it explains that preflight OPTIONS requests are sent, what specific misconfigurations are detected, and critically clarifies the meaning of 'unknown' and 4xx responses. This is exactly the kind of context that helps an agent interpret results correctly.

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 dense but every sentence earns its place: purpose, method, output format, and critical interpretation caveats are all included without redundancy. It is front-loaded with the core purpose and structured logically.

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 tool's moderate complexity, the description is complete: it covers what the tool does, how it works, what it returns, and how to interpret ambiguous or error-like results. The output schema exists, and the description complements it with risk-level and per-origin result context.

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 already covers both parameters with 100% coverage, so the baseline is 3. The description does not add significant parameter-level detail beyond what the schema provides, though it does reinforce that multiple origins are tested by default.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Test a URL for CORS misconfigurations' and enumerates specific detection categories. It is specific and actionable, but it does not explicitly differentiate itself from the sibling tool 'cors_checker', so it stops short of a 5.

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 clear context for when the tool is appropriate: testing URLs for CORS misconfiguration issues. It does not mention alternatives or exclusions, but the intended use case is unambiguous and well-scoped.

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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