The fabric that connects AI to your organization's tools, data, and rules — woven per user, per team, per company
7-Step No-Code Wizard: Name & describe → Paste OpenAPI spec → Select tools → Configure auth (OAuth2, API Key, Bearer, mTLS) → Set scopes & permissions → Test in sandbox → Publish. Supports REST, gRPC, GraphQL, SOAP, and WebSocket APIs.
Visual Designer: Drag engines, plugins, and connectors on the SVG canvas. Toggle security settings visually. Save as blueprints to Cosmos DB.
Code option: Write Python, create a manifest.json defining tools, resources, prompts, and security requirements. Package as a ZIP.
Auto-generation: Import any OpenAPI spec → auto-generates plugin with tools, connectors, credential fields, and security toggles. No coding required.
Upload: Drag a ZIP into the Visual Designer or upload via POST /api/plugins/upload
Auto-registration: The MCP engine reads the manifest, registers all tools with FastMCP, creates connector entries, and sets up knowledge indexes.
2-click install: Upload → Enable. That's it. No server restarts needed.
Visual Designer: Configure everything from the drag-and-drop SVG canvas — attach connectors, toggle security, manage scopes with visual cards and inline controls.
Connectors: Define which external services the plugin talks to. Each connector has credential fields (API keys, tokens, OAuth). Auto-generated from OpenAPI specs via the Plugin Builder.
Credentials: Set at user, group, or org level. The vault cascade resolves the right key at runtime.
Security toggles: Enable/disable write operations, require credentials, set rate limits (backed by Valkey cache), enable audit logging — all per plugin.
Knowledge sources: Ingest from multiple sources — PDFs, URLs, wikis, databases, blob storage. Each source is chunked, embedded, and indexed in Azure AI Search. Scoped by user/group/org.
Flip the power switch → the plugin's tools become available in the agent loop. The LLM can now discover and call them.
Disable: Flip it off → tools instantly disappear from the LLM's toolset. No prompt changes, no code changes. The security wrapper blocks all calls.
Hot-reload: No pod restart needed. Enable/disable is immediate.
Publish: Package your plugin and list it in the marketplace with description, pricing, and security audit report.
Discover: Browse plugins by industry, capability, or rating. One-click install.
Revenue share: Plugin developers earn 70% of sales. ContextWeaver takes 30% platform fee.
Certified: Plugins undergo security audit before listing. Certified badge = meets ContextWeaver security standards.
When the AI needs context to answer a question, it searches across 5 priority levels. The most specific knowledge wins:
P5 Personal: Your private notes, preferences, saved items. Only you can see these.
P4 Group: Your team's shared knowledge — budgets, protocols, team docs. Visible to team members only.
P3 Organization: Company-wide policies, handbooks, vendor lists. Everyone in the org sees these.
P2 Plugin: Knowledge specific to a plugin — API documentation, tool instructions.
P1 Engine: Global defaults — currency, language, audit policies. Broadest scope.
Deduplication: If the same topic appears at multiple levels, higher priority wins. Personal preferences always override company defaults.
Each piece of knowledge is stored in a scoped index with ACLs. The search layer filters results by the requesting user's identity and group membership.
Index naming: {engine}-{plugin}-{scope}-index
ACL enforcement: Even if someone guesses an index name, the search API checks _user_email and _user_groups before returning results.
Connectors define HOW a plugin talks to an external service. Each connector specifies credential fields, OAuth flows, and endpoint URLs.
Per-user credentials: Each user can configure their own API keys for a connector. The vault cascade finds the right key at runtime.
OAuth support: Google, Microsoft, GitHub OAuth flows built in. Users click "Connect with Google" and their token is stored in the vault.
Plugin Builder: The 7-step no-code wizard auto-generates connectors from any OpenAPI spec → auto-generate a connector with all required fields.
When a tool needs an API key, the vault checks three levels in order:
1️⃣ User key — Does this specific user have their own key? (most secure)
2️⃣ Group key — Does their team/department have a shared key?
3️⃣ Org key — Is there a company-wide fallback key?
If no key found at any level → tool call is blocked. The AI reports "credentials required" and suggests the user configure their key in settings.
Storage: Azure Key Vault (production), HashiCorp Vault, or local encrypted file. The AI never sees raw keys.
Every toggle is enforced in the MCP engine's security wrapper — at the code level, not in prompts. No amount of clever prompting can bypass them.
Per-plugin: Enable/disable, require credentials, allow/block write operations, rate limiting, audit logging.
Per-engine: Enable/disable entire engine, block cross-engine calls, require MFA for admin, maintenance mode, audit all calls.
Visual Designer: Toggle switches are visible right on the canvas. Click 🔐 to toggle auth, 🔒 for read-only, 💰 for approval required.
Sources: Upload PDFs, paste URLs, connect wikis (Azure DevOps, Confluence), query databases (SQL, NoSQL), or link blob storage (Azure Blob, S3, GCS). Each source type has a dedicated ingestion adapter.
Chunk: Documents are semantically split into overlapping chunks optimized for retrieval. Chunk size and overlap are configurable per source.
Embed: Each chunk is embedded into a high-dimensional vector using Azure OpenAI embeddings (ada-002 or text-embedding-3). Supports custom embedding models.
Index: Vectors are stored in Azure AI Search (or Elasticsearch/ChromaDB) with full-text + vector hybrid search. Each index is scoped by user/group/org with ACL enforcement.
One-click: Select sources \u2192 click Ingest \u2192 the pipeline runs automatically. No code, no configuration files. Progress is shown in real-time.
Incremental: Re-ingesting a source only processes new or changed content. Unchanged chunks are skipped.