Use MCPFusion Developer Prover with your AI.
Connect your account once and let the AI you already use work with it, without building another integration. LLMs have never been trained on MCPFusion. They use raw z.object(), skip Presenters, mix semantic verbs, and violate MVA layering. This capability teaches the framewo
Developed, maintained, and hosted by Vinkius.
MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED
Waiting for input…
Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.
Complete set · 1 capability
The complete MCPFusion Developer Prover capability set.
These are the exact actions your AI can choose when you ask it to work with MCPFusion Developer Prover.
01
1 capability in this set.
Part of 1 available through MCPFusion Developer Prover.
- 01
Validate mcpfusion implementation
MVA is a CONCEPTUAL separation of responsibilities, NOT a folder naming convention. Before writing or validating MCPFusion code, call this capability to PROVE architectural compliance. Key rules: (1) EVERY entity uses defineModel() with m.casts(). NEVER raw z.object(). defineModel() provides m.hidden(), m.fillable(), m.timestamps(), m.guarded(), .toApi(). z.object() loses all of these, (2) EVERY capability that returns data uses .returns(Presenter). The Presenter validates, strips hidden fields, injects rules, renders UI. createPresenter().schema(Model) or definePresenter({ schema: Model }), (3) SEMANTIC VERBS: f.query() for reads (readOnly, cacheable, safe to retry). f.mutation() for writes (destructive, requires confirmation). f.action() for neutral operations (calculations, validations), (4) Capability INPUT uses .withString()/.withNumber()/.withBoolean()/.withEnum(). NOT z.object(). For many params: .withStrings({...}), .withOptionalStrings({...}). For model-driven input: .fromModel(Model, "create"), (5) .handle() auto-wraps data with success(). NEVER call success() manually, (6) RESPONSIBILITY SEPARATION: Model, View, and Agent logic in separate modules. not mixed in one file. Additional layers (e.g. engine/ for pure business logic) are perfectly valid, (7) .instructions() on every capability for AI-First guidance, (8) f.error() for self-healing errors with .suggest(), .actions(), .retryAfter(). If rejected, fix the MCPFusion pattern violation. Structured reflection capability for MCPFusion framework development. forces MVA (Model-View-Agent) conceptual compliance: defineModel() enforcement, Presenter attachment, semantic verb correctness. LLMs have never been trained on MCPFusion. they default to raw z.object(), skip Presenters, mix semantic verbs, and violate MVA responsibility separation. This capability teaches the framework through structured reflection. Catches MVA Violated (Model, View, and Agent responsibilities mixed in one module. MVA enforces conceptual separation: Models define data shapes, Presenters handle egress, Capabilities expose to LLMs. Mixing defineModel() and .handle() in one file breaks this contract), Raw Schema Detected (using z.object() instead of defineModel() for entity schemas. const UserSchema = z.object({ name: z.string(), email: z.string() }) loses every MCPFusion feature: no m.hidden() for sensitive fields (password hashes, API keys), no m.fillable() profiles for create vs. update contexts, no m.timestamps() for automatic created_at/updated_at, no m.guarded() for mass-assignment protection, no .toApi() for alias resolution. defineModel(User).casts(m => ({ name: m.string(Name), email: m.string(Email) })) provides all of these. z.object() is Zod. defineModel() is MCPFusion), Presenter Missing (returning raw data from .handle() without .returns(Presenter). a capability that returns { users: [...] } without a Presenter sends raw database objects to the client. Hidden fields (password_hash, internal_id) leak. No UI rendering (echarts, mermaid, summary). No agent suggestions (suggestActions for next steps). No collection limiting (agentLimit). createPresenter().schema(UserModel) or definePresenter({ schema: UserModel }). the Presenter IS the egress gate), and Semantic Verb Wrong (using f.action() for a read-only query or f.query() for a destructive write. f.query() marks the capability as readOnly. it can be safely cached, parallelized, and retried. f.mutation() marks the capability as destructive. agents must confirm before execution, responses are never cached. Using f.action() for everything wastes these semantic guarantees. GET /users is f.query(). POST /users is f.mutation(). A calculator is f.action()). NOTE: MVA is a CONCEPTUAL separation of responsibilities, NOT a folder naming convention. Implementations may organize files however they want (including additional layers like engine/ for pure business logic) as long as Model, View, and Agent responsibilities remain separated. Call once per capability or feature being implemented
Observed, not estimated
844ms average. Fast in production.
MCPFusion Developer Prover is checked daily against the live service.
- Fastest day
- 697ms
- Slowest day
- 1032ms
- 14-day trend
- Improving-18%
Connect your client
One URL. Every client.
Activate the Connector, copy your link, and paste it into the client you already use. 1 capability arrives ready to run.
Preview access · not provider authentication
The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of MCPFusion Developer Prover, so you can see the experience inside your AI.
It does not authenticate your account with MCPFusion Developer Prover. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.
MCPFusion Developer Prover Connector
You're all set. Choose your MCP client and follow the setup instructions.
https://edge.vinkius.com/vk_preview_kq18iP2KBas7gK6lLEfjXD7wkf1LFvR2zOIJQ4BQ/mcpClaude Desktop
Follow the steps below to connect in seconds.
- 1In Claude Desktop, open Settings → Connectors.
- 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
- 3Click Add and start a new chat — MCPFusion Developer Prover capabilities are ready to use.
{
"mcpServers": {
"mcpfusion-developer-prover-mcp": {
"url": "https://edge.vinkius.com/vk_preview_kq18iP2KBas7gK6lLEfjXD7wkf1LFvR2zOIJQ4BQ/mcp"
}
}
}
Claude
ChatGPT
Cursor
VS Code
Windsurf
Claude Code
JetBrains
Cline
Step-by-step instructions for each client are in the guide. How to connect
FAQ
Questions MCPFusion Developer Prover owners ask.
- 01
Does this generate MCP server code?
No. The agent writes the code. This capability VALIDATES that the code follows MCPFusion's MVA architecture. defineModel() for entities, Presenters for egress, semantic verbs for operations, and correct file structure. It teaches the framework through rejection messages.
- 02
Why does the LLM need this if it can read documentation?
Documentation reading is one-shot. the LLM reads once and forgets. This capability forces structured reflection on EVERY capability being built. Each field is a micro-lesson: modelStrategy forces naming m.casts() fields, presenterStrategy forces explaining .returns(), toolDesign forces choosing the right semantic verb. Repetition through obligation, not suggestion.
- 03
What if my MCP doesn't return data (reasoning-only)?
Reasoning MCPs still use MVA. The Model defines the verdict/message shape. The Presenter renders the verdict. The capability forces structured input. Even a capability that computes nothing needs defineModel() for its response and a Presenter for its output. The same architecture applies. the Presenter is the egress contract.
Explore
More in Productivity
TypeScript Excellence Prover AI Connector
AI agents produce unsafe TypeScript loaded with `any` types, @ts-ignore overrides, empty catch blocks, and eve
ViewLaravel Excellence Prover AI Connector
AI agents generate Laravel code with N+1 queries, fat controllers, workarounds, and mass assignment holes. Thi
ViewMigration Strategy Prover AI Connector
An AI recommended a big-bang database migration over the weekend. No dependency map — 7 services read from tha
ViewReversibility Architect Prover AI Connector
LLMs suggest irreversible architectural changes. This engine is a 6-pivot cognitive trap that forces the agent
View
Suggestions
CTO Architect Prover AI Connector
An AI proposed Kubernetes for 50 users, says 'use HTTPS' as a security strategy, and plans database migrations
ViewAWS Solutions Architect Prover AI Connector
A Principal-level AWS Solutions Architect reviewing every cloud decision your AI makes. 20 years of production
ViewType Definition Consistency Checker AI Connector
Ensure structural and type-level synchronization between TypeScript interfaces, Zod schemas, and Pydantic mode
ViewEnvironment Consistency Validator AI Connector
A diagnostic tool to verify development environments against expected configurations.
View
