Enterprise revenue teams continue to face a persistent execution gap. CRM platforms store deal data but offer little guidance on how sellers should act on it. Account plans remain disconnected from pipeline reality, AI-generated recommendations surface without reference to the structured sales processes organizations have defined, and agent outputs default to terminology that conflicts with the frameworks revenue teams actually follow. The result is guidance sellers cannot rely on and a growing distance between revenue strategy and field execution.
Altify, the creator of the Strategic Revenue Execution category and the premier Salesforce-native platform for enterprise B2B sales, has announced the release of Altify MCP v0.1.5. The release represents a significant expansion of Altify's Model Context Protocol capabilities, introducing new AI agent functionality across three areas: Account Planning, Sales Process Manager, and Sales Methodology. These enhancements give sellers, sales managers, and AI agents deeper, more consistent, and more actionable access to the account and opportunity data that drives revenue execution inside Salesforce.
MCP, or Model Context Protocol, is the integration layer that allows AI agents to read and act on structured Salesforce data through natural language. With v0.1.5, the scope of what agents can access inside Altify's Salesforce-native platform expands materially. Agents can now assess account health across the full portfolio, read the qualifiers and closure probabilities for individual opportunities, and adapt every output to the sales methodology the organization has configured.
Account Planning: From Static Plans to Active Intelligence
The v0.1.5 release introduces a suite of account planning capabilities that enable AI agents to assess, analyze, and act on account plan data across four dimensions: Account Health, Account Assessment, Account Insights, and Account Review.
Account Health gives sellers and managers a portfolio-level view of their accounts' standing. A seller can now ask the agent which accounts need attention and receive a structured summary scored against a consistent set of health signals, including data completeness, engagement consistency, objective linkage, and relationship coverage. The agent returns a prioritized list of where to focus account planning effort.
Account Assessment goes deeper into individual accounts by running two complementary analyses simultaneously. The first examines the account plan itself, reviewing the relationship map, insight map, account details, and objectives and actions to surface specific gaps and risks. The second extends the analysis across all opportunities linked to the account, drawing on existing opportunity data to identify deal-level risks and produce prioritized recommendations across the full pipeline.
Account Insights connects the strategic account planning process directly to solution recommendations. Rather than generating generic suggestions, the agent anchors its output in the specific pressures, initiatives, and opportunities recorded in the account's Insight Map.
Account Review introduces AI-assisted preparation for key activities involved in reviewing an account plan. The agent can identify gaps and vulnerabilities, analyze account-level actions to flag what is at risk or overdue, and generate coaching material for an upcoming Test & Improve session. For Chief Revenue Officers and sales managers, the Test & Improve preparation capability structures the review conversation around the most critical areas of the plan.
Sales Process Manager: Grounding Agent Guidance in Structured Process Data
The Sales Process Manager module receives its first MCP read capabilities in this release. The AI agent can now read the full Sales Process Manager data associated with any opportunity that has a sales process assigned. In a single operation, the agent retrieves the assigned sales process and current stage, the complete list of qualifiers and verifiable outcomes across all stages, and the opportunity's closure probability score. The closure probability calculation uses the same formula as the Altify Sales Process Manager page, so the value the agent surfaces always matches what the seller sees when reviewing the opportunity directly.
Sales Methodology: Consistent Alignment Across All MCP Capabilities
The third area of the v0.1.5 release ensures that AI agent outputs reflect the terminology and framework the organization has configured, not Altify's defaults. All Altify MCP capabilities now honor the sales methodology configured for the organization. Every capability draws on the organization's configured customization settings when generating its output. The framework supports Altify's native methodology, MEDDIC, and entirely homegrown variants.
"Enterprise revenue teams perform at their highest level when every part of their execution system reinforces the same methodology," said Nigel Cullington, Chief Marketing Officer, Altify. "With MCP v0.1.5, AI agent outputs now reflect the exact framework, terminology, and process structure that each organization has built, so sellers receive guidance that aligns with how they actually work, not how a default system assumes they work."
Administrators configure custom terminology and preferred frameworks through Salesforce customization records. No code changes or redeployment are required, and changes take effect immediately. If a seller refers to their "org chart" rather than their "relationship map," the agent maps the natural-language intent to the correct system concept and responds accordingly.
Altify MCP v0.1.5 is available now. Documentation for the methodology customization framework is available under Methodology Customization for Altify MCP in the Altify online help. An updated list of the five Altify MCP servers and their capabilities is also available in the Altify online help.
