If a tool, like the meeting-note app Granola, lacks an official MCP for integration, you can write a simple script for your AI agent to execute. The script can fetch data and save it as local files, effectively making any external data source part of the agent's accessible context.
To solve the problem of MCPs consuming excessive context, advanced AI clients like Cursor are implementing "dynamic tool calling." This uses a RAG-like approach to search for and load only the most relevant tools for a given user query, rather than pre-loading the entire available toolset.
By connecting AI coding agents like Claude Code to analytics platforms via MCP, product managers can automate weekly reporting, synthesize qualitative feedback, draft specs, and even generate code prototypes. This integrated stack covers the entire product lifecycle, from insight to initial implementation.
"Skills" in Claude Code are more than saved prompts; they are named functions packaging a prompt, specific execution heuristics, and a defined set of tools (via MCP). This lets users reliably trigger complex, multi-step agentic workflows like deep chart analysis with a single, simple command.
When an AI agent like Claude Code nears its context limit where automatic compaction might fail, a useful hack is instructing it to "write a markdown file of your process and your progress and what you have left to do." This creates a manual state transfer mechanism for starting a new session.
MCP provides a standardized way to connect AI models with external tools, actions, and data. It functions like an API layer, enabling agents in environments like Claude Code or Cursor to pull analytics data from Amplitude, file tickets in Linear, or perform other external actions seamlessly.
By organizing all product documents—PRDs, quarterly plans, research, and meeting notes—into a version-controlled GitHub repository, PMs create a single source of truth. This "product repo" becomes a structured environment that AI agents can easily navigate to access context and generate new artifacts.
Users often fail with MCP by expecting it to handle complex workflows instead of simple tool interactions. A key mistake is connecting too many irrelevant servers, which pollutes the AI's context window with unused tool descriptions and degrades performance. Keep the toolset minimal and relevant to the task.
The most advanced analytics workflow moves beyond manual dashboards to scheduled AI agents. These agents analyze data, synthesize top insights and deviations, and automatically push a report into the team's Slack channel. This frees PMs from routine reporting to focus on strategic action.