Overview
Postly gives OpenRouter-powered assistants an authorized social media execution layer. Route work to the model you prefer, then use Postly MCP tools to discover workspaces, prepare drafts, upload media, schedule posts, publish, and review analytics.
The integration is useful for teams that want model choice without rebuilding social platform connections and publishing controls for every assistant.
Why This Matters
MCP matters because it turns AI from a writing surface into an execution surface. Instead of stopping at generation, AI tools can trigger real product actions through a structured layer that respects permissions, workflows, and business logic.
Preflight Checklist
- Define the workflow you want the AI tool to trigger.
- Map each action to an existing Postly capability.
- Keep permissions and workspace routing inside Postly.
- Default to draft-first behavior when actions could be risky.
- Log and validate every AI-initiated action.
Step-by-Step Playbook
- Open Postly's MCP AI Agents area and choose OpenRouter.
- Create or select the approved Postly connection and copy the displayed endpoint and authentication instructions.
- Add the Postly MCP server to the OpenRouter-powered client or agent runtime.
- Test workspace discovery and draft creation before enabling scheduling or publishing.
- Keep approval and analytics checks in the production workflow.
Implementation Tips
- Use the dedicated endpoint shown in Postly rather than copying credentials between unrelated clients.
- Limit each agent to the workspaces and actions it needs.
- Validate platform-specific copy and media before publishing.
Example MCP Action Pattern
Reusable flow for “OpenRouter MCP Integration for Social Media”
- Intent: user asks the AI to perform a real workflow.
- Tool call: AI selects a defined MCP action.
- Validation: auth, workspace, and role checks run first.
- Execution: Postly backend performs the requested action.
- Result: structured output returns to the AI client.
Design Checklist
- Map tools directly to product primitives.
- Use one shared backend action layer across channels.
- Support both MCP and API packaging where needed.
- Keep AI-triggered actions reversible where possible.
- Bias toward draft-first execution for content workflows.
Postly Workflow
In Postly, MCP should expose the product’s existing capabilities rather than invent a new execution system. That means drafts, scheduling, approvals, calendars, accounts, and analytics can be made available across AI-native and integration surfaces while Postly stays the source of truth for execution.
Metrics to Watch
- Tool usage: which MCP actions get used most often.
- Workflow completion: how often AI-generated intent becomes a completed action.
- Approval rate: how many AI-triggered drafts move through review successfully.
- Time saved: whether AI-triggered flows reduce execution time.
- Error rate: how often auth, validation, or workflow failures occur.
Troubleshooting Common Issues
- Too much logic in MCP: move business logic back into Postly services.
- Unsafe actions: default to drafts and approvals instead of direct publishing.
- Permission mismatches: enforce workspace and role checks before execution.
- Generic tool design: define clearer, narrower action schemas.
Related Guides
Frequently Asked Questions
- Can an OpenRouter agent schedule social media posts with Postly?
- Yes. After an authorized MCP connection is configured, the agent can use the Postly tools exposed to it for drafts, media, scheduling, publishing, status, and analytics.
Next Steps
Start by exposing one high-value Postly workflow through MCP, then validate how often users complete that flow from an AI surface. From there, expand into adjacent actions like approvals, scheduling, queue checks, and analytics.