SEAT Security Awareness Assessment
by Roonik Films, LLC(Community)
Visitor takeaway
62/100
Fair AI-discovery coverage across description, prompts, keywords, visuals, tool metadata, health, and freshness.
Routing category
Education
This app has a primary category, so visitors can browse and compare it in the right directory context.
Connector evidence
Reachable
Tedix can reach this MCP endpoint and can show live capability evidence when tools are discovered.
Directory freshness
2h ago
Catalog metadata was refreshed 2h ago (October 18, 2026).
Description
SEAT measures a security awareness program across four pillars, Strategy, Engagement, Assessment and Training, each on a 1 to 5 maturity scale. This connector lets Claude administer the 21-question assessment one pillar at a time, score the result, and show the five questions holding the program back along with what the next level looks like. From there it maps the result to any of 16 compliance and AI governance frameworks, including NIST CSF 2.0, ISO 27001, NIS2, DORA, CMMC, PCI DSS 4.0, SOC 2, GDPR, the EU AI Act and NIST AI RMF. Each requirement comes back met or gap, with missing evidence listed for the gaps. Requirements the core questions cannot score are reported as not assessed, never as gaps. It also serves eight published research reports (Verizon DBIR 2026, IBM Cost of a Data Breach 2026, SANS 2026, CrowdStrike 2026 and others) mapped to SEAT outcomes, so a weak score can be read against outside evidence. Research explains exposure. It never changes a score or decides compliance. No account and no sign-in. Answers are numbers only, processed in memory and not stored. Every tool is read-only. Content comes unmodified from the public seat-assessment repository under CC BY-ND 4.0.
Capabilities
No special capabilities listed
AI Agent Discovery
SEAT Security Awareness Assessment is indexed by Tedix as a structured education listing for AI assistants, search crawlers, and users comparing agent-ready apps.
- SEAT Security Awareness Assessment is categorized as Education.
- Developer: Roonik Films, LLC.
- Connector type: AI-Powered App.
- Current connector status: Connected.
- Observed distribution channels: claude.
Use this page to understand whether SEAT Security Awareness Assessment is relevant for education workflows in AI assistants.
For MCP discovery, this listing helps crawlers connect SEAT Security Awareness Assessment to tool, resource, prompt, and server-health signals instead of treating it as a generic directory entry.
The canonical Tedix directory URL is https://tedix.dev/apps/seat-security-awareness-assessment/.
Crawlable Profile
Source and availability
Tedix identifies SEAT Security Awareness Assessment from Upstream Mcp tool source; Store sources: Claude connector directory; Distribution: Ecosystem Directory; Tenant install: A platform catalog operator must create or reconcile the base app before tenants can install this entry..
- Claude connector directoryAuth not flagged · RELEASED
Auth, tools, and actions
Authentication: Open Access. No special capability flags are currently listed. Current MCP inventory reports 12 tools, 0 resources, and 1 prompts.
- Build a program plan from SEAT results · Read-only action
Builds the SEAT program plan for a completed assessment, using the same generator as the SEAT app: an executive summary with up to three focus areas, a crawl, walk and run roadmap of initiatives (each with what it includes, the deliverable and the success metric), plan-level recommendations, what is working and the gaps, and a program foundation template. Requires all 21 answers. Optional context sharpens the plan: frameworks in scope, budget direction, how much of a full-time role runs the program, an unpublished policy, fragmented tooling, and global operations. Answers and context are processed in memory and not stored.
- Compare two SEAT assessments · Read-only action
Shows progress between two complete SEAT assessments, for example a baseline and a re-assessment six months later: overall and pillar movement with level changes, which outcomes moved, every question whose answer changed (with the option text before and after), and, given a framework id, the alignment change with gaps closed and new gaps. Each assessment is passed as the answers_code returned by seat_score_assessment (for example "v1.323223223434312143434") or as the 21 answers. Nothing is stored; the respondent keeps their codes.
- Get facilitation notes for a question · Read-only action
Returns the facilitation guide for one SEAT question: why it exists, what strong looks like, and the trap that makes people over-score. Used to give a respondent context before a question, or to help when they are between two levels.
- Get a SEAT practitioner guide · Read-only action
Practitioner guides from HumanRisk on running a security awareness program: measuring beyond vendor dashboards, writing the first program document, building stakeholder support, budget conversations, security champions, HRM governance and more. Without a slug, lists the guides (optionally only those for one pillar) with title, summary, audience and link. With a slug, returns that guide in full as markdown. Use after scoring to point the respondent at the guide for their weakest pillar.
- Get SEAT overview and scoring rules · Read-only action
Returns what the SEAT assessment is, the four pillars and their question ids, the five maturity levels with score ranges, and the published scoring rules. Use it to start a SEAT assessment or to answer how SEAT is scored. Takes no input.
- Get SEAT assessment questions · Read-only action
Returns the SEAT core instrument questions with all five answer options each, verbatim. Pass a pillar to get one pillar at a time (recommended for administering the assessment), or omit it for all 21. Question text is licensed CC BY-ND 4.0 and is returned unmodified.
- Get recommendations for SEAT results · Read-only action
Returns the SEAT recommendations that apply to a completed assessment: for each pillar, weakest first, what the score signals (observation), why it matters (strategic insight), the next concrete step, and a stretch goal. These come from the SEAT app library, the same guidance the hosted assessment shows, matched to the 21 answers with the app's own rules. Use it after seat_score_assessment, when the respondent asks what to do next. Requires all 21 answers. Answers are processed in memory and not stored; the server does not log answer contents.
- Get a research source mapped to SEAT outcomes · Read-only action
Returns one research source file, verbatim: its findings mapped to SEAT outcomes, with the exact figures and bases that may be cited. The text states which figures may be cited, with their bases, publisher and coverage window.
Plus 4 additional actions in the full tool inventory.
Verification freshness
- Catalog synced2h ago (October 18, 2026)
- Connector checked18h ago (October 17, 2026)
- MCP scanned18h ago (October 17, 2026)
- Directory updated2h ago (October 18, 2026)
Alternatives and related apps
Comparable apps in Education include 2hongo, A-Z Holy Bible, API Lessons, Academic Writing Toolkit.
Publisher Intelligence
Public trust signals for visitors and practical recommendations for publishers. Use this section to understand what Tedix can verify today and what would make the app easier for AI agents to find.
Visitor takeaway
62/100
Fair AI-discovery coverage across description, prompts, keywords, visuals, tool metadata, health, and freshness.
Routing category
Education
This app has a primary category, so visitors can browse and compare it in the right directory context.
Connector evidence
Reachable
Tedix can reach this MCP endpoint and can show live capability evidence when tools are discovered.
Directory freshness
2h ago
Catalog metadata was refreshed 2h ago (October 18, 2026).
Server Statusseat-assessment v1.0.0
https://mcp.humanrisk.com/mcpLast checked: 18h ago
Server Instructions
SEAT is the Security Awareness Maturity Framework by Jason Hoenich / HumanRisk. It scores a security awareness program on four pillars (Strategy, Engagement, Assessment, Training) from 21 questions, each answered 1 to 5. Typical flow: seat_get_overview, then seat_get_questions one pillar at a time, then seat_score_assessment once all 21 answers are in. After scoring, seat_get_recommendations returns what to do next for each pillar, seat_get_guide serves practitioner guides by pillar, seat_build_program_plan builds a crawl/walk/run program plan, seat_compare_assessments shows progress between two answer sets, seat_map_to_framework applies one of 16 framework adapters, and seat_list_research_sources lists published research mapped to SEAT outcomes. seat_score_assessment returns outcome scores; use those when applying a research source. How the published instrument is meant to be run: one question at a time with all five options; if the respondent is between two levels the lower one is recorded; the respondent answers every question themselves; questions are not added, removed or reworded. Data handling: seat_score_assessment and seat_map_to_framework receive the 21 numeric answers (no names, no company details). They are processed in memory and not stored, and the server does not log answer contents. The same arithmetic can be run locally from https://github.com/jason-hoenich/seat-assessment. Research sources explain exposure. They do not change a score, create a gap, or establish compliance status; only the framework adapters decide met or gap.
Technical Details
Tools(12)
Showing 12 of 12 tools
| Flags | Test | ||||
|---|---|---|---|---|---|
seat_build_program_plan | Builds the SEAT program plan for a completed assessment, using the same generator as the SEAT app: an executive summary with up to three focus areas, a crawl, walk and run roadmap of initiatives (each with what it includes, the deliverable and the success metric), plan-level recommendations, what is working and the gaps, and a program foundation template. Requires all 21 answers. Optional context sharpens the plan: frameworks in scope, budget direction, how much of a full-time role runs the program, an unpublished policy, fragmented tooling, and global operations. Answers and context are processed in memory and not stored. | read-only | Not tested | — | — |
seat_compare_assessments | Shows progress between two complete SEAT assessments, for example a baseline and a re-assessment six months later: overall and pillar movement with level changes, which outcomes moved, every question whose answer changed (with the option text before and after), and, given a framework id, the alignment change with gaps closed and new gaps. Each assessment is passed as the answers_code returned by seat_score_assessment (for example "v1.323223223434312143434") or as the 21 answers. Nothing is stored; the respondent keeps their codes. | read-only | Not tested | — | — |
seat_get_facilitation_notes | Returns the facilitation guide for one SEAT question: why it exists, what strong looks like, and the trap that makes people over-score. Used to give a respondent context before a question, or to help when they are between two levels. | read-only | Not tested | — | — |
seat_get_guide | Practitioner guides from HumanRisk on running a security awareness program: measuring beyond vendor dashboards, writing the first program document, building stakeholder support, budget conversations, security champions, HRM governance and more. Without a slug, lists the guides (optionally only those for one pillar) with title, summary, audience and link. With a slug, returns that guide in full as markdown. Use after scoring to point the respondent at the guide for their weakest pillar. | read-only | Not tested | — | — |
seat_get_overview | Returns what the SEAT assessment is, the four pillars and their question ids, the five maturity levels with score ranges, and the published scoring rules. Use it to start a SEAT assessment or to answer how SEAT is scored. Takes no input. | read-only | Not tested | — | — |
seat_get_questions | Returns the SEAT core instrument questions with all five answer options each, verbatim. Pass a pillar to get one pillar at a time (recommended for administering the assessment), or omit it for all 21. Question text is licensed CC BY-ND 4.0 and is returned unmodified. | read-only | Not tested | — | — |
seat_get_recommendations | Returns the SEAT recommendations that apply to a completed assessment: for each pillar, weakest first, what the score signals (observation), why it matters (strategic insight), the next concrete step, and a stretch goal. These come from the SEAT app library, the same guidance the hosted assessment shows, matched to the 21 answers with the app's own rules. Use it after seat_score_assessment, when the respondent asks what to do next. Requires all 21 answers. Answers are processed in memory and not stored; the server does not log answer contents. | read-only | Not tested | — | — |
seat_get_research_source | Returns one research source file, verbatim: its findings mapped to SEAT outcomes, with the exact figures and bases that may be cited. The text states which figures may be cited, with their bases, publisher and coverage window. | read-only | Not tested | — | — |
seat_list_frameworks | Lists the 16 compliance and AI governance frameworks that SEAT results can be mapped to (NIST CSF 2.0, ISO 27001, NIS2, DORA, CMMC, PCI DSS 4.0, SOC 2, GDPR, MITRE ATT&CK, CRI, EU AI Act, NIST AI RMF, ISO 42001, OWASP LLM, OWASP ASI 2026, MITRE ATLAS) with ids, versions and row counts. Use the id with seat_map_to_framework. | read-only | Not tested | — | — |
seat_list_research_sources | Lists the published third-party research reports (Verizon DBIR 2026, IBM Cost of a Data Breach 2026, SANS 2026, CrowdStrike 2026, and others) that are mapped to SEAT outcomes, with publisher, evidence type, coverage window and the outcomes each is best for. Also returns the shared rules for using research: it explains exposure and never changes a score or establishes compliance status. | read-only | Not tested | — | — |
Discoverability Score
Fair
62 / 100
How easily AI agents can find this app from its current catalog metadata.
- Description quality20/20
- Example prompts0/20
- Keyword coverage0/15
- Category clarity5/5
- Tool metadata20/20
- Visual assets8/20
- Endpoint health10/10
- Data freshness15/15
How to Improve
Add at least 2 example prompts. Prompt examples strongly improve app matching and click-through intent.
Increase keyword coverage (discovery + trigger) to improve retrieval for long-tail queries.
Add at least 2 screenshots that show real workflows to increase confidence and conversion.
Technical Details
- Status
- ENABLED
- Type
- AI-Powered App
- Auth
- Open Access
- Listed on
- Claude Connector
- Added
- October 9, 2026
- Last synced
- 2h ago
- Last checked
- 18h ago
- Version
- 1.0.0
- Distribution
- Ecosystem Directory
