JSON 데이터 쿼리 및 조작을 위한 JSON 모델 컨텍스트 프로토콜(MCP) 서버 구현입니다. 이 서버를 통해 LLM은 표준화된 도구 세트를 통해 JSON 데이터와 상호 작용할 수 있습니다.
지엑스피1
Related MCP server: MCP Database Server
질문
확장된 작업을 사용하여 JSONPath 구문을 사용하여 JSON 데이터 쿼리
입력:
url (문자열): JSON 데이터 소스의 URL
jsonPath (문자열): 선택적 작업이 포함된 JSONPath 표현식
필터
조건을 사용하여 JSON 데이터 필터링
입력:
url (문자열): JSON 데이터 소스의 URL
jsonPath (문자열): 기본 JSONPath 표현식
condition (문자열): 필터 조건
슬라이싱 : $[0:5] , $[-3:] , $[1:4]
정렬 : $.sort(price) , $.sort(-price)
Distinct : $.distinct()
변형 :
맵: $.map(fieldName)
평평하게 만들기: $.flatten()
유니온: $.union([1,2,3])
교차: $.intersection([1,2,3])
케이스 : $.toLowerCase() , $.toUpperCase()
테스트 : $.startsWith('test') , $.endsWith('test')
검색 : $.contains('test') , $.matches('pattern')
수학 : $.math(+10) , $.pow2()
반올림 : $.round() , $.floor() , $.ceil()
함수 : $.abs() , $.sqrt()
형식 : $.format('YYYY-MM-DD')
확인 : $.isToday()
수정 : $.add(1, 'days')
그룹 : $.groupBy(category)
통계 : $.sum(price) , $.avg(price) , $.min(price) , $.max(price)
Claude Desktop 앱과 함께 이 서버를 사용하려면 claude_desktop_config.json 에 다음 구성을 추가하세요.
{
"json": {
"command": "npx",
"args": [
"@gongrzhe/server-json-mcp@1.0.3"
]
}
}또는 패키지가 설치되어 있다면 node 명령을 직접 사용할 수 있습니다.
{
"json": {
"command": "node",
"args": [
"path/to/build/index.js"
]
}
}저장소를 복제합니다
종속성 설치:
npm install프로젝트를 빌드하세요:
npm run build모든 JSONPath 표현식은 루트 객체를 나타내는 $ 로 시작합니다.
배열 인덱스는 0부터 시작합니다.
연산의 문자열 값은 따옴표로 묶어야 합니다.
날짜 연산은 '일', '월', '년' 단위를 지원합니다.
숫자 연산은 기본 산술 연산자(+, -, *, /)를 지원합니다.
MIT
Filter JSON data using conditions
| Name | Required | Description | Default |
|---|---|---|---|
| condition | Yes | Filter condition (e.g. @.price < 10) | |
| jsonPath | Yes | Base JSONPath expression | |
| url | Yes | URL of the JSON data source |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'Filter' implies a read-only operation, the description doesn't clarify whether this tool fetches data from a URL, processes it locally, or has any side effects like caching. It also omits details about error handling, performance, or output format, leaving significant gaps in understanding the tool's behavior.
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, efficient sentence with no wasted words, making it appropriately concise. However, it lacks front-loading of critical information—it doesn't immediately clarify the tool's scope or differentiate it from siblings, which slightly reduces its effectiveness despite the brevity.
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 complexity of a tool that filters JSON data from a URL with three required parameters and no output schema, the description is incomplete. It fails to explain the output format, error conditions, or how the filtering integrates with the data source. Without annotations or an output schema, the agent lacks sufficient context to use the tool effectively.
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%, meaning the input schema already documents all three parameters with descriptions. The tool description adds no additional meaning beyond what the schema provides, such as explaining how parameters interact or providing usage examples. However, since the schema is comprehensive, the baseline score of 3 is appropriate.
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 'Filter JSON data using conditions' states the tool's purpose with a clear verb ('Filter') and resource ('JSON data'), but it's vague about scope and doesn't distinguish from its sibling 'query'. It doesn't specify what kind of filtering or what the output looks like, leaving the agent uncertain about the exact operation.
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 provides no guidance on when to use this tool versus its sibling 'query', nor does it mention any prerequisites or alternative scenarios. Without any context about when this tool is appropriate, the agent must guess based on the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Query JSON data using JSONPath syntax
| Name | Required | Description | Default |
|---|---|---|---|
| jsonPath | Yes | JSONPath expression (e.g. $.store.book[*].author) | |
| url | Yes | URL of the JSON data source |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool queries JSON data but doesn't disclose behavioral traits like error handling, performance implications, rate limits, or what happens if the URL is invalid or JSONPath is malformed. For a tool with no annotations, this is a significant gap in transparency.
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, efficient sentence: 'Query JSON data using JSONPath syntax.' It is front-loaded with the core purpose and has zero waste, making it highly concise and well-structured.
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 complexity of querying external JSON data, the description is incomplete. No annotations exist, and there's no output schema, so the agent lacks information on return values, error cases, or behavioral constraints. The description doesn't compensate for these gaps, making it inadequate for safe and effective use.
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%, with clear descriptions for both parameters ('jsonPath' and 'url'). The description adds minimal value beyond the schema, as it only reiterates the use of JSONPath without providing additional syntax or format details. Baseline 3 is appropriate when the schema does the heavy lifting.
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's purpose: 'Query JSON data using JSONPath syntax.' It specifies the verb ('query'), resource ('JSON data'), and method ('JSONPath syntax'). However, it doesn't explicitly differentiate from the sibling tool 'filter,' which might have overlapping functionality, preventing a score of 5.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tool 'filter' or any other context for usage, such as prerequisites or scenarios where this tool is preferred. This leaves the agent with minimal direction.
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.
v1.0.0filterqueryScored across 2 tools
The tools 'filter' and 'query' both operate on JSON data, which creates some overlap in purpose, as filtering can be seen as a subset of querying. However, the descriptions differentiate them: 'filter' uses conditions (likely simpler, boolean-based operations), while 'query' uses JSONPath syntax (a more expressive, path-based language). This distinction helps reduce confusion, but an agent might still struggle to choose between them for certain tasks.
Both tool names follow a consistent pattern: they are single, lowercase verbs ('filter' and 'query') that clearly indicate actions. There are no deviations in style (e.g., no mixing with snake_case or camelCase), making the naming predictable and easy to understand across the set.
With only 2 tools, this server feels thin for a JSON processing domain, which typically involves operations like parsing, transforming, validating, or merging JSON. The limited scope may force agents to work around gaps, as basic tasks like reading or writing JSON files are not covered, making it under-scoped for a general-purpose JSON server.
The tool set is severely incomplete for JSON processing. While 'filter' and 'query' handle retrieval and selection, there are no tools for creating, updating, validating, or manipulating JSON structures (e.g., add, remove, merge). This leaves significant gaps that will likely cause agent failures when trying to perform common JSON operations beyond simple queries.
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