# Collection Offer Response Drafter AI Agent Connect > Collection Offer Response Drafter MCP acts as a decision engine for formal negotiations. It lets your AI client check incoming offers against your specific rules, like price floors or required conditions, to decide if you should accept, decline, or counter. You can then refine the output to match your professional tone perfectly. ## Overview - **Category:** communication - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_PDpmudkafx6DXx5oYphVUBWlGANtfxcaFGL9C48Q/ai-agent-connect - **Tags:** offers, responses, logic, negotiation-tools, decision-engine ## Description You can stop manually checking every incoming offer against a spreadsheet of rules. This MCP gives your AI client the logic it needs to handle formal negotiations. When an offer comes in, your agent uses this tool to see if the terms actually meet your requirements. It checks things like minimum price thresholds or specific conditions you've set. Once the logic is settled, the agent can draft a response that follows your rules. You aren't just getting a generic reply; you're getting a structured response that respects your constraints. If you need to restrict what the agent can say, you can limit the available response types. You can also adjust the tone of the final message to ensure it sounds exactly how you want it to, whether that's strictly professional or more casual. It turns your AI client into a capable negotiator that follows your playbook every time. ## Tools ### apply_communication_style This tool adjusts the drafted text to hit your specific tone and constraints. It ensures the final message sounds right for the recipient. ### draft_response This tool generates a structured response by weighing an offer against your predefined rules. It decides the best path forward based on your constraints. ### filter_permitted_actions This tool limits the types of responses your agent can choose from. It keeps the AI within a specific set of allowed actions like 'accept' or 'counter'. ### validate_offer_structure This tool checks that incoming offer data is complete and follows the right format. It prevents the agent from acting on broken or missing information. ## Prompt Examples **Prompt:** ``` I received an offer for $500, but my minimum price rule is $600. The permitted responses are 'accept' and 'counteroffer'. Draft a response. ``` **Response:** ``` Thank you for your offer of $500. However, I cannot accept this amount as it is below my minimum requirement of $600. Would you be willing to meet at $650? ``` **Prompt:** ``` Validate if this offer is complete: {'price': 100, 'quantity': 5}. ``` **Response:** ``` The offer is valid and contains all required fields. ``` **Prompt:** ``` The offer is for $1000, which is perfect. The permitted response is 'accept'. Draft the response. ``` **Response:** ``` I am pleased to accept your offer of $1000. ``` ## Capabilities ### Rule-based evaluation Your agent checks offer terms against your specific price and condition rules. ### Data validation The MCP ensures incoming offer data is complete before any decisions are made. ### Response restriction You can limit the agent to only specific actions like countering or accepting. ### Tone adjustment The agent refines drafted text to match your professional or casual style. ### Structured drafting The tool produces organized responses based on the outcome of the evaluation. ## Use Cases ### Vendor Negotiations Compare incoming supplier quotes against your set budget limits automatically. ### Debt Collection Evaluate settlement offers from debtors to see if they meet minimum recovery targets. ### Real Estate Offers Check buyer offers against seller requirements for price and closing dates. ### B2B Sales Draft counteroffers that stay within your authorized discount ranges. ## Benefits - Enforces strict pricing and condition rules during negotiations. - Reduces manual data entry by validating offer schemas automatically. - Maintains consistent brand voice through controlled communication styles. - Limits agent behavior to a set of permitted response types. ## How It Works Connect the MCP to your client and let your agent handle the heavy lifting of offer evaluation. 1. Connect the MCP to your AI client through Vinkius. 2. Provide the incoming offer data to your agent. 3. The agent validates the offer structure and checks it against your rules. 4. The agent drafts a response based on the evaluation result. 5. The agent applies your preferred communication style to the final text. ## Frequently Asked Questions **How does this MCP handle pricing rules?** The agent uses the draft_response tool to evaluate an offer against your specific price thresholds and constraints. **Can I control the tone of the responses?** Yes, you can use the apply_communication_style tool to refine the drafted text to match a professional or casual tone. **What happens if an offer is missing information?** The validate_offer_structure tool checks the data first to ensure it matches the required schema before the agent proceeds. **Can I limit what my agent is allowed to say?** Yes, the filter_permitted_actions tool allows you to restrict the agent to a specific subset of response types. **Which AI clients can use this MCP?** You can use this MCP with any compatible client, including Claude, Cursor, Windsurf, and VS Code. **How does the tool decide to decline an offer?** The `draft_response` tool compares the `offerDetails` against the provided `decisionRules`. If the offer violates a rule, such as a price being too low, the tool selects a decline or counteroffer response. **Can I control the tone of the response?** Yes, you can use the `apply_communication_style` tool to refine the drafted text to match specific formal or casual profiles. **What happens if the offer is missing information?** If the `validate_offer_structure` tool identifies missing required fields, or if `draft_response` cannot evaluate rules due to missing data, the system will trigger an information-request response.