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D3FEND Defense Lookup

d3fend_defense_lookup
Read-onlyIdempotent

Look up a MITRE D3FEND defense technique. D3FEND is the canonical defensive counterpart to ATT&CK — each defense is classified into one of 7 tactics (Model/Harden/Detect/Isolate/Deceive/Evict/Restore) and may target a specific digital artifact (e.g. 'Access Token'). Response includes attack_techniques: the list of ATT&CK T-codes this defense mitigates. Use after d3fend_defense_search for the full record + ATT&CK chain. Returns 404 when the slug is not in the synced D3FEND catalog. Free: 30/hr, Pro: 500/hr. Returns {defense_id, label, uri, parent_label, description, tactic, artifact, attack_techniques, next_calls}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
defense_idYesD3FEND defense slug from the ontology URI fragment (CamelCase), e.g. 'TokenBinding', 'FileHashing', 'CertificatePinning'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already mark this as read-only, idempotent, and non-destructive. The description adds behavioral context by specifying the response structure, 404 behavior, and rate limits, which go beyond the annotation flags.

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?

The description is dense but each sentence earns its place: main action, domain context (tactics), usage guidance, error case, rate limits, and return fields. It is longer than minimal but not wasteful.

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 single-parameter schema, strong annotations, and presence of an output schema, the description is complete: it explains when to use it, what to expect in the response, error handling, and rate limits. No critical gaps remain.

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 covers the single parameter 'defense_id' comprehensively with examples and format hints (100% coverage). The description only references 'slug' indirectly in the 404 sentence, adding no substantial semantic detail beyond what schema already provides.

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 'Look up a MITRE D3FEND defense technique' using a specific verb and resource. It distinguishes itself from sibling tools by advising 'Use after d3fend_defense_search for the full record + ATT&CK chain', making the lookup's role distinct.

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 gives explicit sequencing guidance ('Use after d3fend_defense_search') and notes the 404 error on missing slugs, which helps agents decide when to call it. It does not explicitly state when not to use it, but the context is clear enough.

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.