Upwork MCP

A job review that brings the relevant proof into the proposal

Services: MCP Integrations ; AI Agents ; AI Workflow Automation
Delivery: AIOVIX internal opportunity-review and proposal workflow.

For each promising Upwork job, we need to answer several questions before writing: does the work match our services, what is missing from the brief, which project proves relevant experience, and what should the response focus on?

The job description, service definitions, case studies, and proposal guidance were separate inputs. A proposal draft needed all of them to avoid repeating a general agency introduction.

The MCP workflow retrieves a selected opportunity and extracts its requirements, requested stack, budget signals, and decision criteria.

It then brings in the approved service language and relevant project proof. The proposal draft uses that match, while the opportunity context and next action remain available in the sales workspace.

A person reviews the fit and wording before submission.

The companion service reads official Upwork alert emails through Gmail. Its default workflow checks on a fifteen-minute schedule.

It applies configurable keyword and budget rules. Optional OpenAI screening returns a strict structure containing a review-or-ignore decision, score, service lane, reason, relevant proof, and risks. Only review candidates trigger notifications.

The service marks processed emails and supports Slack, Discord, or Telegram notifications. It does not run the full MCP review or submit proposals automatically.

Separate discovery from qualification. An email alert lacks fields such as current interviews, invitations, payment verification, and hire rate. The screening prompt explicitly prohibits treating those fields as checked.

Ground the proposal in selected evidence. Matching a project to the requested work happens before drafting.

Keep submission manual. The assistant prepares the commercial response; a person approves the commitment.

Retain the next action. The review should leave a follow-up record rather than only a block of generated text.

The delivered workflow connects opportunity context, proof selection, proposal preparation, and follow-up.

The companion automation supplies a concrete front-end screening path with configurable rules and structured AI output. No reply-rate, win-rate, or time-saving number has been supplied for the completed workflow.

MCP-connected opportunity and sales tools; approved AIOVIX service, proof, and proposal records. Companion service: Python, Gmail API, GitHub Actions, OpenAI Responses API with strict JSON-schema output, configurable messaging webhooks.

The companion alert service and the MCP proposal workflow are separate components.

Hypothetical planning example, not a measured project result or a performance forecast. All volumes and timings below are assumed.

Review the brief, choose relevant proof, and approve the proposal draft.

• Workload: 30 qualified opportunities per month.
• Manual handling: 25 minutes per item.
• Assisted handling, including human review: 15 minutes per item.
• Modeled difference: 30 x (25 - 15) = 300 minutes (5 hours) per month.

This excludes unqualified alerts and does not assume a higher reply or win rate. This is potential staff capacity, not cash savings; setup, training, and software costs are excluded.

How to validate: Time a matched set of qualified jobs from opening the full brief to an approved draft. Include proof checks, edits, and discarded drafts; track replies and wins separately.

The right proof before the first proposal draft.
Our Upwork MCP workflow connects a selected job to service positioning, relevant case studies, proposal guidance, and follow-up. A separate email-alert service screens opportunities on a scheduled basis.

We use an Upwork MCP workflow to review the job, find the relevant proof, and prepare a specific proposal. The alert automation screens the email first; the deeper review and final submission stay separate.