docs: add positioning, scenario, and open-infra sections to README

Adds "Why Knowledge Plane?", "How It Works", and "Open Infrastructure"
sections for the Mozilla newsletter audience. Updates Contributing
sections in both README and CONTRIBUTING.md with specific help areas.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: altras

CONTRIBUTING.md

@@ -2,6 +2,16 @@
 
 Thank you for your interest in contributing to KnowledgePlane! We welcome contributions from everyone, whether it is a bug report, feature request, documentation improvement, or code change.
 
+## Areas We Need Help
+
+We're a small team and appreciate contributions in these areas:
+
+- **MCP tools** -- New tools for the MCP server, improved search/retrieval, better tool descriptions
+- **Web dashboard** -- UI improvements, new views (graph visualization, timeline), accessibility
+- **Documentation** -- Tutorials, integration guides, architecture deep-dives
+- **Testing** -- Integration tests, edge case coverage, benchmark improvements
+- **Integrations** -- Adapters for other AI tools and platforms beyond Claude Desktop
+
 ## How to Contribute
 
 ### Development Setup

README.md

@@ -19,6 +19,20 @@
 
 Knowledge Plane is an MCP server that gives AI agents and teams persistent, shared memory. It stores facts as a knowledge graph with vector embeddings, automatically consolidates related facts into knowledge cards, and provides hybrid search (vector + graph traversal). Native MCP protocol support means any MCP-compatible AI tool (Claude, Cursor, etc.) can read and write shared knowledge.
 
+## Why Knowledge Plane?
+
+Memory layers for AI exist (Mem0, Zep, Letta), but they're built around flat vector stores -- good for retrieval, less good for understanding how things relate. Knowledge Plane stores facts as a graph with typed relations, so "Alice manages Bob" is a first-class edge you can traverse, not a blob of text you hope cosine similarity resurfaces. Background workers continuously consolidate related facts into knowledge cards, building institutional knowledge instead of just accumulating embeddings. And it's designed for teams from the start: multi-workspace, multi-agent, multi-tool -- shared memory across your entire AI stack, not just one chatbot.
+
+## How It Works
+
+Your team runs AI agents across different tools -- a dev agent in the IDE, a PM agent in the browser, a support agent in Slack. On Monday, the dev agent learns that the payments API was refactored. On Wednesday, the PM agent is planning a sprint and needs that context. Without shared memory, it doesn't have it. With Knowledge Plane, the dev agent wrote that fact once, the consolidation worker linked it to the payments service card, and now any agent in any tool can find it through search or graph traversal.
+
+## Open Infrastructure
+
+Built on [MCP](https://modelcontextprotocol.io) (Model Context Protocol), the emerging open standard for AI tool integration. Fully self-hostable -- your knowledge stays on your infrastructure. Apache-2.0 licensed, no telemetry, no vendor lock-in. Your data, your rules.
+
+Built by [Camplight](https://camplight.net), a cooperative of AI-native teams across Europe. Used internally to power knowledge sharing across [Juma.ai](https://juma.ai) and Slack-integrated marketing agents.
+
 ## Features
 
 - **Persistent agent memory** -- Facts stored in ArangoDB knowledge graph with vector embeddings
@@ -125,7 +139,13 @@ knowledgeplane/
 
 ## Contributing
 
-We welcome contributions! Please see [CONTRIBUTING.md](./CONTRIBUTING.md) for guidelines.
+We're actively looking for help with:
+- **MCP tool coverage** -- adding new tools and improving existing ones
+- **Dashboard UI** -- the web interface needs polish and new views
+- **Documentation** -- guides, tutorials, and API docs
+- **Testing** -- integration tests, edge cases, benchmark coverage
+
+See [CONTRIBUTING.md](./CONTRIBUTING.md) for setup instructions and guidelines.
 
 ## License