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@gokulr I like this. I spent the past week writing up my product strategy and now the agent has access in markdown, but now this maybe is a little more organized
Users like ProductSpec's MCP Server for AI Coding Agents because it provides handy access to product strategy documents in markdown for their agents.
Based on 2 visible X reactions from 3 accounts; directional sample.
PRODUCTSPEC MCP SERVER We just shipped the ProductSpec MCP server. This is the next step in making Product Specs useful to AI coding agents. The problem is simple: agents can write code fast, but they often don't know the product intent behind the code. They see the repo. They see the issue. They see the prompt. But they usually don't have a durable control file that says: • what problem this work is solving • what is in scope • what is explicitly out of scope • what acceptance criteria must pass • what AI evals should be run • what success looks like after launch That is what ProductSpec is meant to provide. The new MCP server lets coding agents access Product Specs directly as structured tools. An agent can now call: list_product_specs get_product_spec validate_product_spec get_scope get_acceptance_criteria get_ai_evals get_success_metrics get_related_artifacts check_completion_claim The important one is check_completion_claim. Before an agent says “done,” it can ask ProductSpec what still needs to be verified against the Product Spec. That changes the workflow. Without ProductSpec: - Founder or PM writes intent somewhere. - Engineer or agent gets a loose task. - Implementation drifts. - Everyone debates whether the thing that shipped was the thing that was requested. With ProductSpec: - Intent lives in the repo. - The agent reads the same control file as the team. - Scope and acceptance criteria become visible before coding starts. - Completion gets checked against the original product intent. You can configure it in any MCP-compatible coding environment as a stdio server. MCP config: command: npx args: ["--yes", "-p", "@productspec/parser@latest", "productspec", "mcp"] The open source repo now includes: • the ProductSpec standard • parser and validator • GitHub Action • agent skills • Decision Trace • MCP server • (Not in the repo, but there is a free privacy-friendly Git native Product Spec editor at ProductSpec dot io) My view: the next generation of software teams will need a product intent layer. Git stores implementation history. Jira and Linear store work history. Figma stores design artifacts. ProductSpec stores what the team meant to build, why it mattered, and what had to be true before the work was done. That intent now speaks MCP. Builders using coding agents: put a.product-spec.md file next to the work, then make the agent read it before writing code. The control file is where AI-native product development starts.
@gokulr I like this. I spent the past week writing up my product strategy and now the agent has access in markdown, but now this maybe is a little more organized
PRODUCTSPEC MCP SERVER We just shipped the ProductSpec MCP server. This is the next step in making Product Specs useful to AI coding agents. The problem is simple: agents can write code fast, but they often don't know the product intent behind the code. They see the repo. They see the issue. They see the prompt. But they usually don't have a durable control file that says: • what problem this work is solving • what is in scope • what is explicitly out of scope • what acceptance criteria must pass • what AI evals should be run • what success looks like after launch That is what ProductSpec is meant to provide. The new MCP server lets coding agents access Product Specs directly as structured tools. An agent can now call: list_product_specs get_product_spec validate_product_spec get_scope get_acceptance_criteria get_ai_evals get_success_metrics get_related_artifacts check_completion_claim The important one is check_completion_claim. Before an agent says “done,” it can ask ProductSpec what still needs to be verified against the Product Spec. That changes the workflow. Without ProductSpec: - Founder or PM writes intent somewhere. - Engineer or agent gets a loose task. - Implementation drifts. - Everyone debates whether the thing that shipped was the thing that was requested. With ProductSpec: - Intent lives in the repo. - The agent reads the same control file as the team. - Scope and acceptance criteria become visible before coding starts. - Completion gets checked against the original product intent. You can configure it in any MCP-compatible coding environment as a stdio server. MCP config: command: npx args: ["--yes", "-p", "@productspec/parser@latest", "productspec", "mcp"] The open source repo now includes: • the ProductSpec standard • parser and validator • GitHub Action • agent skills • Decision Trace • MCP server • (Not in the repo, but there is a free privacy-friendly Git native Product Spec editor at ProductSpec dot io) My view: the next generation of software teams will need a product intent layer. Git stores implementation history. Jira and Linear store work history. Figma stores design artifacts. ProductSpec stores what the team meant to build, why it mattered, and what had to be true before the work was done. That intent now speaks MCP. Builders using coding agents: put a.product-spec.md file next to the work, then make the agent read it before writing code. The control file is where AI-native product development starts.
Users like ProductSpec's MCP Server for AI Coding Agents because it provides handy access to product strategy documents in markdown for their agents.
Based on 2 visible X reactions from 3 accounts; directional sample.
Ask a question below.
Published answers will appear here.