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Lets AI ask clarifying questions and collect structured human input.
The available material is very limited, but the tool is an open-source MIT project with no required secrets and no declared remote endpoints, with no clear high-risk red flags visible. Caution is still warranted because it is flagged as executing code locally, while community adoption is low and maintenance status is unknown.
The material explicitly states that no keys or environment variables are required, and it does not request API tokens, account credentials, or other sensitive authentication data, so credential exposure appears limited.
No remote endpoint or external service connection is declared; based on the provided material, there is no explicit destination for user data egress. However, with no README, it cannot be fully verified that runtime behavior is entirely offline.
The objective checks flag that it executes code. For an MCP tool, running local server code is a normal capability and not by itself a high-risk signal, but it should still be treated as code running on the host and isolated accordingly.
The description only indicates a Human-in-the-Loop clarification interface and structured user input, without clearly stating any filesystem or database access scope. Since an MCP server typically can access session input data and the documentation is missing, data access boundaries remain unclear and should be minimized.
Positive factors include an auditable open-source GitHub repository and an MIT license, which lower overall risk; however, the source is only a third-party registry entry, community adoption is 0 stars, maintenance is unknown, and the README is absent, so supply-chain confidence is only moderate and source/dependency review is advisable.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Clarify MCP Server" yet — see the docs or source repo.
When the user's request is incomplete, do not assume details. Ask 5 key clarification questions and collect the answers in a structured form before generating the solution.
The AI first asks clarifying questions and returns structured input fields, then proceeds after the user responds.
Before writing the script, confirm the runtime environment, programming language, input/output format, dependency limits, and error-handling requirements, then organize the replies into structured parameters.
It produces targeted follow-up questions and a structured parameter object for downstream code generation.
Design an AI assistant flow for marketing copy generation: first ask for target audience, brand tone, distribution channel, length, and banned words, and record them in a structured format.
It outputs a human-in-the-loop questioning flow and structured field definitions for reliable downstream content generation.
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