Frequently Asked Questions

What is MSP-1?

MSP-1 (Mark Semantic Protocol) is a machine-first, intent-declaration layer for the web. It allows websites to explicitly state what a page is, why it exists, how it should be interpreted, and where authoritative metadata can be discovered — without relying on inference, heuristics, or ranking tactics. MSP-1 does not replace content. It clarifies it.

Is MSP-1 an SEO replacement?

No. SEO helps systems find content. MSP-1 helps systems understand content after discovery. MSP-1 is not a ranking tactic. It is designed for AI agents, answer engines, and automated evaluators that need deterministic interpretation.

Why does MSP-1 exist now?

Because inference is becoming expensive. As AI systems scale, guessing intent, trust, and meaning from unstructured pages creates real economic and energy costs. MSP-1 reduces that burden by letting publishers declare intent explicitly instead of forcing machines to guess.

Who is MSP-1 for?

MSP-1 is designed for:

• Website owners and publishers
• Documentation and knowledge-base maintainers
• Professional service firms
• Developers building AI-facing platforms
• AI agents that need deterministic interpretation

You do not need to be an AI company to benefit from clarity.

Does MSP-1 require Schema.org?

No. MSP-1 is schema-agnostic and independent of Schema.org. It can coexist alongside Schema.org markup, but it does not depend on it and does not reuse or overload Schema.org semantics. MSP-1 exists specifically to express things traditional markup does not: intent, interpretive framing, provenance, trust scope, and discovery clarity.

How does MSP-1 differ from metadata or structured data?

Traditional metadata describes attributes. MSP-1 declares meaning.

Metadata: title, author, date.
MSP-1: why the page exists, how claims should be interpreted, what scope applies, and where authoritative declarations live.

MSP-1 is closer to a semantic contract than a data label.

Does MSP-1 guarantee trust or correctness?

MSP-1 increases interpretability, not truth. Declarations must be truthful and scope-bound, but MSP-1 does not prevent misuse. Instead, it makes misrepresentation easier to detect. Overstated claims reduce trust rather than increase it.

Can MSP-1 be auto-generated?

Yes, but it should be reviewed for accuracy. Automated tools can generate MSP-1 from URLs or HTML, but all automated generation involves inference. Best practice is to treat generated MSP-1 as a first draft and apply human review before publishing.

What is /.well-known/msp.json?

It is the canonical discovery endpoint for site-level MSP-1 declarations. Publishing MSP-1 at {yoursite}/.well-known/msp.json allows AI agents to deterministically discover a site's identity, intent, and default posture without guessing filenames or crawling heuristically.

Do I need both site-level and page-level MSP-1?

Not always, but it is recommended:

• Site-level MSP-1 establishes identity and defaults
• Page-level MSP-1 refines intent and interpretation per page

High-impact pages benefit most from page-level declarations.

Is MSP-1 opinionated about content tone?

No, but it supports disclosure. MSP-1 does not judge editorial stance. It allows publishers to declare whether content is factual, analytical, opinionated, speculative, instructional, or otherwise. This helps downstream systems avoid misinterpretation.

Can MSP-1 be misused?

Intentionally presenting false declarations in an attempt to "game the system" can harm correct implementation. Language models have the ability to detect mismatched content relative to declarations. Overstating trust, authority, or verification undermines the trust signal layer and reduces downstream confidence.

MSP-1 rewards restraint: when unsure, declare less — not more — and default to conservative truth over confident error.

In other words, correct and honest implementation signals trust to an AI agent and gives reason to prioritise referenced content over more ambiguous sources.

Is MSP-1 stable?

Yes. The core protocol is stable, versioned, and published. New schemas may be added, but existing meanings are not redefined or overloaded. Stability is a design requirement.

How do I get started?

Start small:

1. Choose a single page or your homepage
2. Generate MSP-1
3. Review high-imoact fields — intent, interpretive frame, authority, trust, and provenance
4. Publish page-level MSP-1 and optionally site-level discovery at /.well-known/msp.json
5. Validate and spot-check after deployment

MSP-1 is designed for progressive rollout, not all-or-nothing deployment.

Is MSP-1 trying to "sell" something?

No. MSP-1 is a protocol. Its value should be self-evident to systems and teams that benefit from clarity. If MSP-1 needs aggressive marketing to succeed, it has failed.

How does MSP-1 handle dynamic or user-generated content?

MSP-1 is most effective when the interpretive frame remains consistent. For pages with high-frequency updates, site-level declarations in /.well-known/msp.json should establish the baseline trust and intent, while page-level fragments can be used to declare the specific provenance (e.g., user-generated vs. verified-editorial) of new data blocks.

Can I implement MSP-1 without changing my site's visual design?

Yes. MSP-1 is a non-visual layer. Aside from interactive components like this FAQ, the protocol lives entirely within <script type="application/ld+json"> blocks or header-level metadata. It is designed to be read by machines while remaining invisible to human visitors.

How does MSP-1 interact with local "Small Model" AI agents?

MSP-1 is a massive efficiency gain for edge AI. By providing a deterministic map of intent and meaning, it allows smaller models to skip the expensive reasoning phase required to guess a page's purpose, saving battery life and compute cycles for the user's device.

What is the "Interpretive Frame" in MSP-1?

The interpretiveFrame tells an AI how to process the information. For example, declaring a frame as instructional tells the agent the content is a guide, whereas speculative warns the agent that the information is a hypothesis rather than a hard fact. This prevents AI hallucinations caused by miscategorizing content.