휴리스틱 키워드 추출을 사용하여 원시 텍스트 프롬프트를 구조화된 JSON 프롬프트로 변환하는 MCP 서버입니다. API 키가 필요하지 않습니다.
convert_prompt_to_json원시 텍스트 프롬프트를 입력받아 추출된 필드가 포함된 구조화된 JSON 객체를 반환합니다:
purpose — 프롬프트가 달성하려는 목적
goal — 구체적인 희망 결과물
audience — 출력 대상
context — 배경 정보 또는 제약 사항
tone — 원하는 어조/스타일 (격식 있는, 캐주얼한, 기술적인 등)
instructions — 프롬프트에 포함된 단계별 지침
format — 원하는 출력 형식 (목록, 문단, 코드 등)
original_prompt — 그대로 보존된 원시 입력값
edit_prompt_json기존의 구조화된 JSON 프롬프트, 필드 이름, 새로운 값을 입력받습니다. 업데이트된 JSON을 반환합니다.
Related MCP server: simple-prompts-mcp
npm install
npm run buildmcp.json에 추가하세요:
{
"mcpServers": {
"json-promptor": {
"command": "node",
"args": ["json-promptor-mcp/dist/index.js"]
}
}
}입력 프롬프트:
Docker 사용 방법을 설명하는 개발자용 격식 있는 블로그 게시물을 번호가 매겨진 목록으로 작성해 주세요.
출력:
{
"purpose": "write a formal blog post for developers explaining how to use docker, as a numbered list",
"goal": "",
"audience": "developers",
"context": "",
"tone": "formal",
"instructions": "",
"format": "numbered list",
"original_prompt": "Write a formal blog post for developers explaining how to use Docker, as a numbered list."
}Convert a raw text prompt into a structured JSON prompt with extracted fields (purpose, goal, audience, context, tone, instructions, format)
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes | The raw text prompt to convert |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It only states the conversion function without disclosing behavioral traits such as how parsing is done (e.g., AI-based), whether it's deterministic, or any side effects. This is minimally transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence of 18 words that efficiently conveys the tool's purpose without extraneous information. Every word is necessary.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter, no output schema, no annotations), the description adequately covers the core functionality. It could mention potential limitations or error handling, but it is reasonably complete for a straightforward conversion task.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds context about the output structure but does not add additional meaning to the single parameter beyond what the schema already provides ('The raw text prompt to convert').
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (convert), input (raw text prompt), and output (structured JSON with listed fields). It distinguishes from the sibling tool 'edit_prompt_json' by specifying the transformation from raw text to structured format.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use this tool (when you have a raw prompt to structure), but does not explicitly contrast with the sibling tool or state when not to use it. No alternatives or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Edit a field in an existing structured JSON prompt
| Name | Required | Description | Default |
|---|---|---|---|
| structured_prompt | Yes | The structured JSON prompt as a JSON string | |
| field | Yes | The field to edit | |
| value | Yes | The new value for the field |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description indicates mutation ('Edit a field'), but does not disclose return value, validation, side effects, or persistence. This is insufficient for an editing tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence of 9 words, front-loaded with the action, concise and free of unnecessary information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lacks information on return value, validation rules, or error conditions. Given no output schema and annotations, the agent cannot fully anticipate behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents each parameter. The description adds no extra meaning beyond repeating the purpose, meeting the baseline for full coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool edits a field in an existing structured JSON prompt, using a specific verb and resource. It distinguishes from the sibling tool 'convert_prompt_to_json', which likely converts to JSON.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for modifying a single field, but does not explicitly mention when not to use it or suggest alternatives like 'convert_prompt_to_json' for conversion tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0convert_prompt_to_jsonedit_prompt_jsonScored across 2 tools
The two tools are clearly distinct: one converts raw text to a structured JSON prompt, the other edits an existing JSON prompt. There is no ambiguity or overlap.
Both tools follow the verb_noun pattern consistently: convert_prompt_to_json and edit_prompt_json. The naming is clear and predictable.
With only 2 tools, the server is minimal but appropriately scoped for its narrow purpose of converting and editing JSON prompts. However, it could benefit from a few more tools like validation.
The tools cover the core workflow of converting raw prompts to JSON and editing them. Missing operations like validation or export are minor gaps but do not severely hinder the stated purpose.
MCP server for generating rough-draft project plans from natural-language prompts.
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