Model Context Protocol - Dropbox

dropboxJul 20, 20263 min readCurated summary

How our universal content processing platform Riviera evolved for AI and beyond

Riviera evolved from Dropbox’s preview-generation service into a shared content-processing platform used by products including Search, Replay, Sign, and Dash. Its core insight was to compose reusable transformations rather than build separate pipelines for every file type and output. As AI increased demand for consistent document extraction and preparation, Dropbox expanded Riviera’s capabilities and began offering them through APIs and Model Context Protocol tools. ## The Preview Problem - Dropbox supports more than 300 file formats, each requiring outputs such as: - Thumbnails - Full previews - Extracted text - Streaming manifests - Metadata - Building a separate service for every format and output would duplicate logic, dependencies, and operational work. - Configurations and package versions could drift across services, making the system harder to maintain and scale. ## Reusable Transformations as the Foundation - Riviera treats previews as sequences of smaller, reusable transformations. - For example, a PowerPoint preview can be produced by: - Converting the presentation to PDF - Rendering each PDF page as an image - The same PDF-to-image transformation can support PDFs and other workflows requiring page images. - This approach enables new formats and products to reuse existing capabilities instead of starting from scratch. ## Separating Coordination from Execution - Riviera uses a central coordinator to: - Collect and validate requests - Compose transformation workflows - Cache responses - Dispatch jobs to backend workers - Each worker handles a specific transformation, creating a clear unit for maintenance and scaling. - The platform now includes more than 100 capabilities and performs hundreds of thousands of transformations per second. - New formats and transformations can generally be added as plugins without changing the core system. ## From Internal Service to Shared Platform - Other Dropbox teams quickly adopted Riviera when they discovered overlapping content-processing needs. - Machine learning teams reused preview thumbnails for image normalization, avoiding duplicate generation. - Search used Riviera to prepare documents for indexing, while Sign, DocSend, and Replay reused existing transformations. - Dropbox eventually opened the plugin model to product teams, allowing them to add capabilities while the Riviera team maintained the platform’s core architecture. - Replay particularly benefited from Riviera’s complex video transcoding and manipulation capabilities, accelerating product development from months to weeks. ## Supporting AI Workloads - Dash introduced greater demand for reliable document preparation before AI processing. - AI systems require content to be transformed into consistent, machine-readable representations, including: - Extracted text - Data from scanned pages - File metadata - Normalized versions of hundreds of file types - These are fundamentally content-transformation challenges rather than AI-model challenges. - Because Riviera already supported many formats and transformations, Dash could build on existing infrastructure instead of creating a separate document-processing system. ## Broader Availability - Dropbox is making Riviera’s capabilities available to external developers and design partners. - Access is provided through APIs and Model Context Protocol tools. - The platform is intended for applications such as content management, document automation, search indexing, and AI document processing. Riviera’s evolution demonstrates the value of a shared transformation platform: reusable workers reduce duplication, centralized coordination improves reliability, and each new capability benefits multiple products. For teams building content-heavy or AI-powered applications, using standardized transformation infrastructure can be more efficient than maintaining format-specific pipelines independently.

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Model Context Protocol AI Distributed Systems Plugin Architecture+3

dropboxJun 12, 20263 min readCurated summary

How Dropbox uses MCP and Dash to close the design-to-code security gap

Dropbox found a significant gap between security design reviews and implementation. Only 12% of implementing pull requests linked back to their original threat models, and the median delay between review and code submission was about five weeks. To close this gap, Dropbox built a system using Dash, Model Context Protocol (MCP), and foundational models to automatically retrieve relevant security requirements and compare them with code during review. ## The Design-to-Code Gap - Threat models document risks, attack scenarios, and agreed-upon mitigations during security review. - These documents often remain in wikis or documentation systems, while implementation happens later through pull requests. - At Dropbox: - Only 12% of implementing PRs linked to their original design review. - Among 79 verified pairs, 54% of PRs were opened more than a month after the review. - The median delay was approximately five weeks, with some delays exceeding 11 months. - Only 29% of PRs were opened within two weeks of the security review. - About 15% of design reviews were filed retroactively, suggesting that some security-sensitive work was not identified early enough. ## Why Existing Tools Fall Short - Static analysis can detect whether certain security patterns or controls exist. - It generally cannot determine whether those controls satisfy the specific requirements agreed upon during design review. - Manual linking between PRs and threat models depends on engineers remembering additional workflow steps. - Reminder bots may improve compliance temporarily, but adherence tends to decline. - The core problem is not a lack of security documentation; it is the difficulty of making that existing context available during implementation. ## Dash and MCP as a Context Bridge - Dash already indexes Dropbox content and connected applications, including threat models and engineering documentation. - Dropbox used Dash’s MCP server to let an AI security agent search and read this content. - MCP provides a common interface for bringing multiple context sources into one agent session. - When a PR is opened, the agent retrieves relevant threat models and supporting documents. - A foundational model then compares the documented requirements with the proposed code. - For example, it can identify whether code implementing an endpoint enforces authentication required by the corresponding threat model. - Unlike traditional static analysis, this approach evaluates code against documented security intent, not just known code patterns. ## Integrating Security into Code Review - Dropbox surfaced the system directly within the existing code-review workflow rather than creating a separate security process. - This allows developers and reviewers to receive relevant security context where implementation decisions are already evaluated. - The same mechanism can potentially identify missing reviews when code appears security-sensitive but has no corresponding threat model. Dropbox’s approach shows how retrieval and AI reasoning can reconnect design decisions with implementation. Organizations can apply the same pattern beyond security—for example, to compliance requirements, privacy reviews, accessibility standards, or other design-to-code checks.

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Model Context Protocol Large Language Models Code Review Static Analysis+2

dropboxMay 21, 20263 min readCurated summary

Introducing Nova, our internal platform for coding agents

Nova is Dropbox’s internal cloud platform for running coding agents across the software development lifecycle. Rather than building separate tools for coding, CI debugging, migrations, and operational tasks, Dropbox created a shared platform that supports interactive sessions and autonomous workflows within its monorepo and infrastructure. The platform grounds agent changes in real builds and tests, making AI assistance more reliable and easier to integrate into engineering workflows. ## The Case for a Shared Platform - Engineering work includes repetitive but important tasks such as: - Debugging CI failures - Updating dependencies - Improving test coverage - Fixing flaky tests - Managing migrations and operational work - Different tasks require different interaction models: - Interactive chat for developer-driven work - Asynchronous workflows for long-running remediation and automation - Dropbox’s environment has specialized requirements: - A large monorepo - Bazel for builds and tests - Caching and remote execution - On-premises infrastructure - Dropbox-specific validation workflows - Off-the-shelf coding agents were designed primarily for local development and did not naturally fit this environment. ## How Nova Runs Coding Sessions - Each session runs in an isolated environment using a specific snapshot of the codebase. - Callers provide: - The repository commit - A task description - Optional validation commands - Iteration limits and branch settings - Nova can run builds and tests after an agent proposes a change. - If validation fails, the results are sent back to the agent so it can continue troubleshooting. - This creates a feedback loop of: - Propose a change - Validate it in the real environment - Correct failures - Repeat as needed - Nova supports multiple coding agents behind a common interface. - Engineers can access it through: - A web interface - A command-line client - An API - Internal scripts and services - The platform also provides prompt evaluation, observability, feedback collection, skills, plugins, and MCP integrations for accessing systems such as logs and monitoring tools. ## Deterministic Code Publication - Nova keeps code publication outside the agent. - Each session is limited to a single branch. - This makes active work and publication status predictable. - It avoids the complexity of agents creating and managing multiple branches. - The deterministic model simplifies automation such as: - Running tests - Rebasing onto the main branch - Tracking which changes belong to each session ## Engineering Workflows Using Nova ### Developer-Driven Sessions - Engineers use Nova’s web interface for quick fixes and prototypes without disrupting local work. - Validation commands can use Bazel selectivity tools to target the relevant compile and test dependencies. - Slack discussions can be carried into Nova sessions, preserving context and reducing manual setup. ### Flaky Test Remediation - Dropbox built Deflaker, a durable workflow connected to Athena, its flaky-test detection system. - Deflaker gathers examples of a test passing and failing. - It sends the associated logs to Nova. - The agent analyzes the evidence, identifies a likely cause, and proposes a fix. - This demonstrates how Nova can combine investigation, context gathering, and code changes in a longer-running automated process. ## Practical Takeaway Dropbox’s experience suggests that coding agents are most useful when embedded in existing engineering systems rather than treated as isolated code-generation tools. A shared platform like Nova can support many workflows while preserving consistent execution, validation, context, and observability.

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Model Context Protocol Observability CI/CD Slack+4

dropboxJan 28, 20263 min readCurated summary

Engineering VP Josh Clemm on how we use knowledge graphs, MCP, and DSPy in Dash

Dropbox VP Josh Clemm argues that useful workplace AI requires a unified context engine capable of securely understanding and retrieving information across many SaaS applications. Dropbox Dash combines custom connectors, multimodal content processing, knowledge graphs, hybrid search, and personalized access-control-aware ranking to make that possible. Clemm favors indexed retrieval over purely federated approaches because preprocessing enables richer context, faster search, and company-wide access, though it requires substantial engineering and infrastructure. ## Building Dash’s Context Engine - Custom connectors crawl third-party applications while handling: - Rate limits - API differences - Application-specific permissions and ACLs - Incoming content is normalized, often into Markdown, and enriched with: - Titles and metadata - Extracted links - Embeddings - Other key information - Different media types require different processing: - Documents can be text-extracted and indexed. - Images may require CLIP or multimodal models. - PDFs combine text, figures, and other elements. - Audio is transcribed. - Videos may require scene-by-scene multimodal analysis when dialogue is insufficient. - Dash models relationships between content as a knowledge graph: - Meetings can connect to documents, participants, transcripts, and previous notes. - Cross-application relationships provide richer context for search and agents. - Data is stored in secure systems using: - BM25 lexical search - Dense-vector storage - Hybrid retrieval - Multiple ranking stages personalize results and enforce user-specific permissions. ## Indexed Retrieval Versus Federated Retrieval - Federated retrieval queries external systems at runtime. - Its advantages include: - Fast initial implementation - Minimal storage requirements - Relatively fresh data - Easy addition of MCP servers and connectors - Its drawbacks include: - Inconsistent API speed, quality, and ranking - Limited access to company-wide content - Expensive post-processing and reranking - Large token usage when agents reason over returned results - Indexed retrieval preprocesses content during ingestion. - Its advantages include: - Access to shared company connectors - Enriched datasets created offline - Faster queries - More opportunities for recall and ranking experiments - Its costs include: - Significant custom connector development - Freshness challenges - High hosting costs - Difficult storage and architecture choices involving vector search, BM25, hybrid retrieval, or graph RAG. ## Making MCP Practical at Scale - MCP can simplify tool integration, but tool definitions consume substantial context-window space. - Large tool descriptions and retrieval results contribute to context rot and reduce agent effectiveness. - Dash aims to limit context usage to roughly 100,000 tokens. - MCP-based agents can also be slow: simple queries may take up to 45 seconds, while direct index retrieval returns results within seconds. - Dropbox’s approach is to wrap its index in a consolidated “super tool,” reducing the need to expose many separate tools to the agent. ## Broader AI Engineering Practices - The talk also covers Dropbox’s use of: - LLMs as evaluators or judges - Prompt optimization with DSPy - Tool calling and MCP design - These techniques complement the underlying context engine rather than replacing the indexing, enrichment, graph modeling, and permission systems required for reliable workplace AI. A practical takeaway is that organizations building AI over proprietary data should treat retrieval as a full data-platform problem. Start with robust connectors and permissions, enrich content before retrieval, model relationships across sources, and use MCP selectively where indexed retrieval can provide faster and more controlled results.

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Model Context Protocol RAG Multimodal AI Dspy+4

dropboxNov 17, 20253 min readCurated summary

How Dash uses context engineering for smarter AI

Dash evolved from a traditional RAG search system into an agentic AI that can interpret information, plan tasks, and act on users’ behalf. Dropbox’s experience shows that better agent performance comes not from adding more tools and data, but from carefully engineering context: limiting choices, filtering for relevance, and delegating complex work to specialized agents. The central conclusion is that precise, timely context improves reasoning speed, accuracy, and efficiency. ## From Search to Agentic AI - Dash initially combined semantic and keyword search to retrieve documents and generate concise answers. - Users began asking it to interpret, summarize, and act on retrieved information. - This required Dash to plan and execute multi-step tasks rather than simply search and summarize. - The resulting challenge was determining which information and tools the model actually needed at each stage. ## The Cost of Too Many Tools - Every tool adds descriptions and parameters to the model’s context window. - More tools expand the model’s decision space, potentially causing slower or less reliable choices. - Tool definitions also consume tokens, increasing cost and reducing room for reasoning. - Longer-running tasks suffered from “context rot,” where accumulated tool-call information degraded accuracy. - Model Context Protocol (MCP) standardizes tool descriptions, but does not eliminate the problem of excessive context. ## Limiting Tool Definitions - Dash found that exposing retrieval tools from many services—such as Confluence, Google Docs, and Jira—created confusion. - Instead of requiring the model to choose among numerous APIs, Dash consolidated retrieval into one purpose-built tool backed by its universal search index. - A single retrieval interface: - Simplifies planning - Reduces tool-selection errors - Keeps the context window focused - Provides consistent access across connected services - The same principle shaped Dash’s MCP server, which exposes retrieval through one lean tool to applications such as Claude, Cursor, and Goose. ## Filtering Context for Relevance - Retrieved information is not automatically useful for the task at hand. - Dash combines data from multiple sources in a unified index and uses a knowledge graph to connect people, activity, and content. - These relationships help rank results according to the query and the user’s context. - By filtering results before presenting them to the model, Dash ensures that each piece of supplied context is relevant. - Precomputing the index and graph allows runtime retrieval to remain fast and focused. ## Using Specialized Agents for Complex Tasks - Some tools require substantial instructions and examples to use correctly. - Dash Search became complex because query construction involves: - Understanding user intent - Mapping intent to index fields - Rewriting queries for semantic matching - Handling typos, synonyms, and implicit context - Adding these instructions directly to the main planning agent consumed context that could otherwise support broader reasoning. - Dash therefore moved search into a specialized agent: - The main agent decides when searching is necessary. - The search agent independently constructs the query using its dedicated prompt. - This division lets the main agent focus on the overall task while the specialist handles search details. Dash’s approach recommends treating context as a limited engineering resource. Use a small number of well-designed tools, pre-filter information for relevance, and delegate technically demanding subtasks to specialized agents rather than overwhelming one general-purpose model.

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Model Context Protocol AI Agents RAG Context Engineering+3