You're already doing context engineering. Now do it with real tools.
Every AI interaction starts with a context window — system prompts, conversation history, retrieved documents, tool definitions. You're already deciding what goes in and what stays out. Promptmark gives that work a proper home: versioned, testable, portable across models.
How Promptmark fits
Templates as parameterized context
A good context window isn't static — it changes with the user, the task, the moment. Template variables let you define which parts of your context are fixed and which swap at runtime: {{persona:select:analyst,advisor,critic}}, {{retrieved_docs:text}}, {{conversation_summary:text}}. One context architecture, many executions.
Version-controlled context iterations
Context engineering is iterative — you add a constraint, remove an example, reorder instructions, and measure what changes. Every edit creates an automatic snapshot. Diff any two versions to see exactly what shifted in your context design. Restore a previous version when a change degrades performance.
Multi-model context validation
The same context behaves differently across models. A system prompt that works perfectly on Claude may get ignored by Gemini or misinterpreted by GPT. Run identical context configurations against 300+ models and compare where instructions hold, where tone drifts, and where the model fills gaps you didn't intend.
Playbooks for multi-step context orchestration
Complex AI workflows aren't one prompt — they're sequences where each step's output becomes the next step's context. Playbooks chain prompts together with branching logic, variable passing between steps, and @prompt references that pull versioned context from your library.
Conversations for context validation
Run multi-turn dialogues against your context configuration to test how instructions hold across conversation depth.
66 MCP tools for context distribution
Your AI agents and dev tools fetch context directly from your library through MCP — render templates with live variables, check the latest version, validate inputs.
External tools via Connections
Connect to external MCP servers and pull their tools into your context workflows. Playbook steps can call databases, APIs, or services through MCP-connected tools — keeping your context pipeline integrated end to end.
Collections for context architectures
Group system prompts, few-shot examples, retrieval templates, and tool definitions into collections by application or context role.
Example workflow
Audit your current context
Import existing system prompts, instruction sets, and few-shot examples into Promptmark. Organize them into collections by application, model target, or context role.
Parameterize the variable parts
Replace hardcoded values with typed template variables. Persona, retrieved documents, user profile, task instructions — anything that changes between executions becomes a variable.
Test across models and configurations
Run your context configuration against your target models with controlled inputs. Compare where instructions hold and where they degrade. Version control captures every experiment.
Compose into workflows
Build playbooks that chain context through multi-step sequences. Each step receives context from the previous one, adds its own instructions, and passes results forward.
Distribute via MCP
Connect your AI agents and dev tools to your context library through MCP. 66 tools let agents fetch, render, and test prompts directly — no copy-paste, no stale versions.
Your context window is too important to manage in a text file
Versioned context configurations, parameterized templates, multi-model validation, and MCP distribution. The tools context engineering has been missing.
Start managing your context — free