MCP vs. The Rest: Comparing Semantic Protocols for a Smarter Web

The internet’s future depends on semantic understanding and context. But how do today’s protocols stack up? Let’s compare Model Context Protocol (MCP) with other leading semantic solutions.

What Drives the Need for Semantic Protocols?

As digital ecosystems grow, the gulf between raw data and meaningful information deepens. Classic data exchange suffices for files or spreadsheets, but starts crumbling when knowledge, reasoning, and intent must be relayed. Semantic protocols fill this gap, enabling ecosystems that not only exchange data but also interpret, contextualize, and interoperate meaningfully.

Key challenges:

Data interoperability: Moving beyond compatible file formats towards consistent meaning across platforms.
Context-awareness: Interpreting intent, relationships, and nuance.
Granular permissions: Sharing only the needed meaning, with privacy and control.
Scalability: Supporting tens of thousands of sources, agents, and models.

Model Context Protocol (MCP) at a Glance

MCP has emerged as a robust protocol designed to share, reference, and transfer context-rich knowledge between agents, applications, and repositories. Its cornerstone concept is the context chunk, encapsulating meaning, provenance, metadata, and even operational rules in a standardized, portable way.

MCP’s design priorities:

Contextual integrity: Each chunk self-describes its semantic boundaries.
Inter-agent interoperability: Emphasis on third-party exchange, not just storage.
Repository onboarding: Easy to plug in new knowledge bases.
Semantic versioning: Tracking and updating knowledge context over time.

Snapshot of Leading Emerging Semantic Protocols

Here’s a who’s-who of challenger protocols in the semantic space:

1. Solid

Developed with guidance from the Web’s inventor, Tim Berners-Lee, Solid focuses on personal data pods decoupled from apps, emphasizing user data ownership and decentralized identity.

2. ActivityPub

The protocol behind Mastodon and the Fediverse, ActivityPub standardizes how activities and objects (posts, likes, etc.) are federated and understood across social networks.

3. JSON-LD

A lightweight Linked Data extension for JSON, JSON-LD makes semantic annotations possible within everyday web apps by adding context framing to basic payloads.

4. W3C PROV-O

The PROV Ontology models provenance data, enabling transparent accountability for digital artifacts and workflows.

5. Schema.org

Not a wire protocol, but a semantic vocabulary embedded in web pages to enable better indexing and richer search results.

Core Comparisons: MCP vs. the Field

How does MCP’s approach contrast with these peers? Let’s break down key dimensions.

1. Model for Knowledge Repositories

Protocol	Knowledge Encapsulation	Portability	Decentralized?	Self-Describing?
MCP	Context chunks, versioned	High	Yes	Yes
Solid	Personal data pods	High	Yes	Partially
ActivityPub	Activities, objects	Medium	Yes	Partially
JSON-LD	Context-annotated JSON	Good	Decentralized*	Yes
W3C PROV-O	Provenance entities, agents	Good	Yes	Yes
Schema.org	Structured HTML markup	Moderate	Decentralized	Partially

MCP is unique in treating repository content as semantically rich, modular “context chunks.”

Other approaches tend to focus on mapping data or user actions, with less emphasis on the dynamic versioning and cross-agent interoperability that MCP makes central.

2. Interoperability and Federation

MCP: Embeds mechanisms for federating access across repositories, including robust identity and permissions frameworks.
Solid: At its core, Solid is about personal data federation, relying on identity systems like WebID. It tackles user data, but less so rich domain knowledge or reasoning.
ActivityPub: Strong for activity streams and social objects, but weaker for transferring context or complex relationships more deeply than posts or user actions.
JSON-LD: Ubiquitous and popular, but more a serialization schema than an interoperability protocol—great for annotation, not governance or permissions.
W3C PROV-O: Excels at provenance but delegates access and federation to other standards.
Schema.org: No wire exchange; semantic structuring for crawlers and search, with minimal real-time collaboration.

MCP often stands out by integrating controls and modular context exchange, instead of leaving key features “out of protocol.”

3. Handling Granular Permissions and Provenance

MCP: Each context chunk contains cryptographic proofs, access governors, and provenance trails as first-class citizens.
Solid: Heavily invested in user-centric permission models (Access Control Lists, OIDC), though often at the container or document level.
ActivityPub: Basic controls via actor-object-activity model, but lacks depth for nuanced knowledge domains.
JSON-LD: Leverages external permission schemes; annotations are visible but not inherently governed.
W3C PROV-O: Premier focus on provenance, excelling in transparency.
Schema.org: Little in-built security or permissioning.

For ecosystems where intellectual property, fine-grained privacy, or legal compliance are essential, MCP and Solid lead the charge. MCP adds operational depth for automated/AI agent scenarios.

4. Semantic Versioning and Change Tracking

Tracking changes, especially for living knowledge, is crucial for collaborative intelligence.

MCP: Versioning is core; context chunks can reference previous states, branching, and semantic diffs. Enables “time travel” and rollback at a conceptual level.
Solid: Relies on standard document versioning; context diffs often manual.
ActivityPub: Activities logged, but no structured versioning for underlying objects.
JSON-LD: No native versioning; up to the application builder.
W3C PROV-O: Tracks derivations and processes, enabling partial versioning.
Schema.org: No standard for change tracking.

MCP’s robust context lineage offers advantages for applications that demand transparency and long-term reproducibility.

5. Context Sensitivity and Knowledge Representation

MCP: Context modeling is built-in—not only what is said, but where, when, and why it matters. Relationships, provenance, intent and operational contracts travel with each chunk.
Solid: Context limited to user- and resource-level metadata.
ActivityPub: Context as thread/actor/reply chains; deeper semantics are outside scope.
JSON-LD: Lets developers inject context, but no governance over depth/quality.
W3C PROV-O: Context within workflows, less so for raw facts.
Schema.org: Mostly rigid, domain-focused vocabularies.

Applications demanding explainable knowledge and detailed context see MCP as a front runner—in contrast to protocols aimed at mere data movement.

6. Real-World Integration and Developer Adoption

MCP: Early adopter phase but gaining traction in knowledge graph repositories, scientific collaboration tools, and AI agent platforms.
Solid: Supported by a galvanized privacy-first community and some enterprise pilots. Tooling maturing, but mainstream adoption is slow.
ActivityPub: Rampant across Fediverse social web and decentralized publishing.
JSON-LD: Baked into mainstream application stacks; crucial for SEO, linked data, and certain search applications.
W3C PROV-O: Adopted in scientific reproducibility, blockchain provenance, and regulated industries.
Schema.org: Omnipresent for website marking; critical for search engine visibility.

MCP aims for a horizontal “contextual backbone,” while others often target specific user cohorts (social, web, science), or specific pain points.

7. Extensibility and Customization

MCP: Intentionally modular. New types of context chunks, schemas, and rulesets can be added without fragmenting the system.
Solid: Extensible via Pods and app-specific vocabularies, but structure remains oriented around personal artifacts.
ActivityPub: Open extension model supporting custom object types (beyond basic activities), but interoperability depends on widespread support.
JSON-LD: Vocabularies and contexts are infinitely extensible, sometimes resulting in interoperability headaches.
W3C PROV-O: Extendable within ontology boundaries—specialization encouraged.
Schema.org: Provides a slowly evolving core set, with some room for extension for verticals.

MCP strikes a middle ground: tightly governed extensibility, aiming for broad alignment but not wild fragmentation.

_{Photo by Scott Rodgerson on Unsplash}

8. Performance and Scalability

MCP: Designed for high-performance batch and real-time context transfer, with explicit support for distributed, federated deployments.
Solid: Emphasizes privacy and autonomy; performance varies by pod implementation.
ActivityPub: Optimized for social scale (millions of activities). Suits timeline, not heavy context.
JSON-LD: Lean and lightweight, but not designed for high-throughput knowledge sharing.
W3C PROV-O: Scalable for metadata, less so for real-time or big data.
Schema.org: Not an active participant in transfer; it is passive structure.

For organizations seeking enterprise-scale semantic knowledge sharing, MCP’s performance features are often decisive.

Practical Examples: How Protocols Differ in Use

Global Supply Chain Audit Trail

MCP: Each batch of goods emits a context chunk with provenance, ownership, compliance, and lifecycle; access governed per-chunk.
Solid: Every actor controls data about its own steps, but lacks a seamless cross-enterprise audit trail.
ActivityPub: Could model events (shipped, received), but struggles with detailed traceability.
JSON-LD: Encodes static details, but not dynamic, governed trails.
W3C PROV-O: Excellent at representing processes, complements MCP well.
Schema.org: Marked up supply chain pages for web visibility, but no federated tracking.

Cross-Organizational Science Collaboration

MCP: Research blocks, hypotheses, results, and their evolution are context chunks, linked and collaboratively curated, supporting reproducibility.
Solid: Each scientist owns her dataset, with manual cross-linking.
ActivityPub: Shares announcements or papers, but lacks deep knowledge exchange.
JSON-LD: Good for annotating publications but lacks operational workflow.
W3C PROV-O: Supports provenance, but not direct knowledge mobility.

Where Are the Gaps?

One can see how no single protocol covers all needs. Solid prioritizes personal agency, ActivityPub connects conversations, and Schema.org aids discoverability. MCP takes on the hard job of marrying transfer, context, intent, permission, and versioning for both human and agent actors.

That said, MCP does demand steeper learning for implementers, and as a fresher protocol, its ecosystem is still maturing.

The Future: Complement or Supplant?

Will MCP eventually merge with or replace its rivals, or will the future web rely on combining protocols for complementary needs?

The most likely outcome is a layered semantic ecosystem:

MCP anchoring context mobility and robust cross-actor data flows
Solid empowering user-held knowledge and consent
ActivityPub knitting user activity and social context
W3C PROV-O fortifying audit trails
JSON-LD and Schema.org boosting discoverability and simple linkage

Cross-protocol “bridges” and translation layers are already surfacing, as projects see value in blending MCP’s contextual depth with the broad reach or simplicity of its peers.

Key Takeaways for Builders and Decision Makers

Assess business needs.
- For deep knowledge and context exchange, MCP delivers on versioned, permissioned, provenance-rich exchange.
- For privacy-first personal data, Solid is best.
- For public-facing, discoverable data, mark up with Schema.org and JSON-LD.
- For social/collaborative workflows, ActivityPub fits.
Interoperability is not a switch, but a journey. A pragmatic integration of multiple protocols often delivers best real results.
No protocol is future-proof. Standards evolve as the web’s needs change; MCP is positioning itself as both foundational and flexible.

Conclusion: Building a Meaningful, Connected Data Future

As digital systems grow more complex and interdependent, MCP’s context-centric philosophy is pushing protocols from simple data plumbing toward semantic, operational collaboration. Its finer granularity, embedded controls, and deep context come at the cost of complexity—but for those solving real, multi-agent, knowledge-sharing problems, MCP’s toolkit feels purpose-built. Most organizations will use a suite of protocols, with MCP increasingly anchoring the backbone of their semantic architecture.

In the race toward a smarter, more connected web, context is the new currency—and MCP is shaping up to be its universal ledger.

External Links

A2A vs MCP: Two complementary protocols for the emerging agent … An Unbiased Comparison of MCP, ACP, and A2A Protocols - Medium Model Context Protocol (MCP) vs Semantic Kernel (SK) - Medium MCP, RAG, and ACP: A Comparative Analysis in Artificial Intelligence Comparison of MCP and ANP: What Kind of Communication …