Context MCP: Internal
Problem
Critical context for understanding a codebase—decisions in Slack, docs in Confluence, tickets in Jira, logs in Datadog—lives outside the repository. Agents don’t have access to this by default, leading to incomplete reasoning.
MCP attempts to solve this, but in practice it doesn’t scale: multiple MCP servers consume 10–30% of the context window, lack proper permissioning, and often flood the agent with irrelevant data, causing context rot.
What we're releasing
Context MCP: Internal is a single MCP endpoint backed by a knowledge graph that unifies context across all systems—code, Slack, Confluence, Jira, logs, and more—into one structured interface.
Instead of exposing many tools, it provides a single access point that intelligently selects only the relevant context for each task while enforcing permissioning and access control.
Expected outcome
Agents get the right context, not just more context—leading to better decisions, higher-quality outputs, and fewer errors.
Context MCP: Internal
Problem
Critical context for understanding a codebase—decisions in Slack, docs in Confluence, tickets in Jira, logs in Datadog—lives outside the repository. Agents don’t have access to this by default, leading to incomplete reasoning.
MCP attempts to solve this, but in practice it doesn’t scale: multiple MCP servers consume 10–30% of the context window, lack proper permissioning, and often flood the agent with irrelevant data, causing context rot.
What we're releasing
Context MCP: Internal is a single MCP endpoint backed by a knowledge graph that unifies context across all systems—code, Slack, Confluence, Jira, logs, and more—into one structured interface.
Instead of exposing many tools, it provides a single access point that intelligently selects only the relevant context for each task while enforcing permissioning and access control.
Expected outcome
Agents get the right context, not just more context—leading to better decisions, higher-quality outputs, and fewer errors.