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Scan Headers

scan_headers
Read-onlyIdempotent

Perform live HTTP GET and analyze security headers: CSP, HSTS, X-Frame-Options, X-Content-Type-Options, Permissions-Policy, Referrer-Policy. Use to audit live website headers; use check_headers to validate headers you already have. Free: 30/hr, Pro: 500/hr. By default header values are truncated to 500 chars (CSP can exceed 4 KB on large sites); pass include='full' for the full raw value. Returns {headers_present, headers_missing, findings, total_score}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesDomain to scan live HTTP headers for (e.g. 'example.com', 'api.github.com')
includeNoDetail level. Default ('') returns slim findings — raw header values capped at 500 chars with total_value_length carrying the honest pre-truncation length. Pass 'full' to restore the full raw value (useful for inspecting full CSP directives on sites like GitHub where the CSP header exceeds 4 KB). Allowed: '' or 'full'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.7/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, etc.), the description discloses key behavioral details: header values are truncated to 500 chars by default, and include='full' restores the full raw value (with a note about large CSP headers). It also states the exact return object shape. This gives the agent actionable knowledge about result fidelity and option effects.

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 compact yet information-dense. It opens with an immediate statement of purpose, then lists checkable headers, usage context, an alternative, rate limits, a behavioral caveat, and the return format. Every sentence earns its place and there is no fluff.

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?

Despite the tool's moderate complexity, the description covers purpose, usage, alternatives, performance characteristics (rate limits), output structure, and a detailed behavioral nuance (truncation). An output schema exists, so return fields are further documented. The description is complete enough for an agent to select and invoke the tool 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?

The schema already covers 100% of the parameters with highly detailed descriptions, including the exact behavior of include. The tool description restates some of this (e.g., 'pass include=full') but adds no new semantic meaning beyond what the schema provides. Per the rubric, with high schema coverage, 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 states that the tool performs a live HTTP GET and analyzes security headers, listing the specific headers checked (CSP, HSTS, etc.). It also distinguishes itself from the sibling check_headers by noting that scan_headers audits live websites while check_headers validates existing headers. This is a specific verb+resource with explicit sibling differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells the agent when to use this tool ('Use to audit live website headers') and provides an alternative ('use check_headers to validate headers you already have'). It also includes rate limit information (Free vs Pro), giving concrete guidance on practical constraints.

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

A4.5/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, with differences between lookup/search/scan/audit for each domain. However, some overlap exists (e.g., email_mx vs email_security_posture, scan_headers vs contrast_scan) which could cause occasional confusion. Overall, boundaries are well-defined.

Naming Consistency5/5

Tool names follow a consistent verb_noun pattern (e.g., cve_lookup, check_headers, bulk_cve_lookup) with all lowercase underscores. Variations like kev_detail or ssl_check are minor and still predictable. No chaotic mixing of conventions.

Tool Count4/5

54 tools is high but justified by the broad cybersecurity scope (CVE, ATLAS, D3FEND, Sigma, domain, email, IOC, scanning). Some redundancy exists (e.g., three email-related tools), but the count is not excessive given the API's comprehensive feature set.

Completeness5/5

The tool set thoroughly covers the threat intelligence and domain investigation lifecycle: CVE/KEV/exploit/CWE, ATLAS/D3FEND/Sigma, DNS/WHOIS/SSL/subdomains, email security, IOC enrichment, and active scanning. No significant gaps are apparent for the stated cybersecurity purpose.