여러 플랫폼에서 프로그래밍 리소스를 검색하고 액세스할 수 있는 도구를 제공하는 모델 컨텍스트 프로토콜 서버입니다. 이 서버는 주요 개발자 플랫폼과 통합되어 LLM이 관련 코드 예제, 문서 및 패키지를 찾을 수 있도록 지원합니다.
Stack Overflow - 프로그래밍 Q&A
MDN 웹 문서 - 웹 개발 문서
GitHub - 코드 및 저장소 검색
npm - JavaScript 패키지 레지스트리
PyPI - Python 패키지 인덱스
search_stackoverflow프로그래밍 질문과 답변을 보려면 Stack Overflow를 검색하세요.
매개변수:
query (필수): 검색 쿼리 문자열
limit (선택 사항): 최대 결과(1-10, 기본값: 5)
반환: 점수, 답변 수 및 발췌문이 포함된 형식화된 질문 목록
결과는 1시간 동안 캐시됩니다.
search_mdn웹 개발 문서를 보려면 MDN 웹 문서를 검색하세요.
매개변수:
query (필수): 검색 쿼리 문자열
반환: 요약 및 링크와 일치하는 상위 5개 MDN 문서
결과는 1시간 동안 캐시됩니다.
search_github저장소와 코드 예제를 모두 보려면 GitHub을 검색하세요.
매개변수:
query (필수): 검색 쿼리 문자열
language (선택 사항): 프로그래밍 언어로 필터링
limit (선택 사항): 카테고리당 최대 결과 수(1-10, 기본값: 5)
반환: 두 섹션:
별점 순으로 정렬된 상위 저장소
저장소 컨텍스트가 있는 관련 코드 파일
결과는 1시간 동안 캐시됩니다.
search_npmJavaScript 패키지를 찾으려면 npm 레지스트리를 검색하세요.
매개변수:
query (필수): 검색 쿼리 문자열
limit (선택 사항): 최대 결과(1-10, 기본값: 5)
반환: 버전, 설명 및 다운로드 통계를 포함한 패키지 정보
결과는 1시간 동안 캐시됩니다.
search_pypiPython 패키지를 찾으려면 PyPI를 검색하세요.
매개변수:
query (필수): 검색 쿼리 문자열
반환: 버전, 작성자, 링크를 포함한 자세한 패키지 정보
결과는 1시간 동안 캐시됩니다.
search_all모든 플랫폼을 동시에 검색하여 포괄적인 결과를 얻으세요.
매개변수:
query (필수): 검색 쿼리 문자열
limit (선택 사항): 플랫폼당 최대 결과(1-5, 기본값: 3)
반환: 모든 플랫폼의 결합된 결과:
Stack Overflow 질문과 답변
MDN 문서
GitHub 저장소 및 코드
npm 패키지
PyPI 패키지
결과는 1시간 동안 캐시됩니다.
참고: 더 빠른 응답을 위해 모든 검색을 병렬로 실행합니다.
Related MCP server: Context7 MCP Clone
노드.js >= 20.11.0
npm >= 10.0.0
선택 사항: 더 높은 API 속도 제한을 위한 GitHub 개인 액세스 토큰
Smithery를 통해 Claude Desktop용 Code Research Server를 자동으로 설치하려면:
지엑스피1
저장소를 복제하고 종속성을 설치합니다.
git clone https://github.com/nahmanmate/code-research-mcp-server.git
cd code-research-server
npm install서버를 빌드하세요:
npm run buildMCP 설정 구성:
MCP 설정 파일에 서버 구성을 추가합니다.
VSCode: ~/.vscode-server/data/User/globalStorage/rooveterinaryinc.roo-cline/settings/cline_mcp_settings.json
클로드 데스크탑:
MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
윈도우: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"code-research": {
"command": "node",
"args": ["/absolute/path/to/code-research-mcp-server/build/index.js"],
"env": {
"GITHUB_TOKEN": "your_github_token" // Optional: Prevents rate limiting
},
"disabled": false,
"alwaysAllow": []
}
}
}참고: /absolute/path/to 저장소를 복제한 실제 경로로 바꾸세요.
변경 사항에 따라 자동 다시 빌드되는 개발의 경우:
npm run watch서버는 강력한 오류 처리를 구현합니다.
각 플랫폼에 대한 API별 오류 메시지
GitHub API에 대한 속도 제한 처리
서비스 이용 불가에 대한 우아한 대체
API 부하를 줄이기 위한 캐시된 응답
MCP 서버는 stdio를 통해 통신하므로 디버깅이 어려울 수 있습니다. 자세한 요청/응답 모니터링을 위해서는 MCP Inspector를 사용하세요.
npm run inspector검사관은 다음을 제공합니다.
실시간 요청/응답 모니터링
도구 실행 추적
오류 스택 추적
성과 지표
디버깅 인터페이스에 접근하려면 브라우저에서 제공된 URL을 방문하세요.
결과는 node-cache 사용하여 캐시됩니다.
기본 TTL: 1시간
쿼리/제한 조합당 별도의 캐시 키
플랫폼별 캐싱 전략
메모리 효율적인 스토리지
저장소를 포크하세요
기능 브랜치 생성
변경 사항을 커밋하세요
지점으로 밀어 넣기
풀 리퀘스트 만들기
AGPLv3
Search all platforms simultaneously
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query | |
| limit | No | Maximum results per platform (1-5, default: 3) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'simultaneously', hinting at parallel execution, but doesn't disclose critical behaviors like rate limits, authentication needs, error handling, or what 'all platforms' entails (e.g., which ones). For a search tool with no annotation coverage, this leaves significant gaps in understanding how it operates.
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 zero waste. It's front-loaded and appropriately sized for its purpose, making it easy to parse quickly without unnecessary elaboration.
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 searching multiple platforms, no annotations, and no output schema, the description is incomplete. It doesn't explain what 'platforms' are, how results are returned (e.g., aggregated, formatted), or any behavioral constraints. For a tool with 2 parameters and sibling alternatives, more context is needed to guide 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 documentation for 'query' and 'limit' parameters. The description adds no additional meaning beyond the schema, such as query syntax examples or platform-specific nuances. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, but no extra value is provided.
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 'Search all platforms simultaneously' states the action (search) and scope (all platforms), but it's vague about what 'platforms' refers to. It doesn't specify the verb's target (e.g., 'code repositories', 'documentation') or distinguish from siblings like search_github which target specific platforms. The purpose is understandable but lacks specificity.
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?
No guidance is provided on when to use this tool versus the sibling tools (search_github, search_mdn, etc.). The description implies it searches across multiple platforms, but it doesn't specify if this is for broad queries, cross-platform needs, or as an alternative to individual platform searches. Without explicit when/when-not instructions, the agent must infer usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search GitHub for repositories and code
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query | |
| language | No | Filter by programming language | |
| limit | No | Maximum number of results per category (default: 5) |
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. It states the tool searches GitHub but doesn't describe what the search returns (e.g., repository metadata, code snippets, or both), any rate limits, authentication needs, or error handling. For a search tool with no annotation coverage, this leaves significant gaps in understanding its 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 extremely concise with a single sentence ('Search GitHub for repositories and code'), front-loaded with the core purpose. There is zero wasted text, making it efficient and easy to parse, though this conciseness comes at the cost of missing contextual details.
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 complexity (searching a major platform with 3 parameters) and lack of annotations and output schema, the description is incomplete. It doesn't explain what the search returns, how results are structured, or any limitations, leaving the agent with insufficient context to use the tool effectively beyond basic parameter input.
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?
The description adds no parameter-specific information beyond what the input schema provides, which has 100% coverage with clear descriptions for query, language, and limit. Since the schema does the heavy lifting, the baseline score of 3 is appropriate, as the description doesn't compensate with additional context like query syntax examples or language filtering details.
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 ('Search GitHub') and the target resources ('repositories and code'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its siblings (like search_npm or search_pypi) beyond specifying the GitHub platform, missing an opportunity to highlight GitHub-specific search capabilities versus other code search tools.
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 the sibling tools (search_all, search_mdn, search_npm, search_pypi, search_stackoverflow). It doesn't mention alternatives, prerequisites, or specific contexts where GitHub search is preferred over other search tools, leaving the agent to infer usage based on the platform name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search MDN Web Docs for web development documentation
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool searches MDN Web Docs but doesn't describe what the search returns (e.g., articles, code snippets), how results are formatted, any rate limits, authentication needs, or error handling. This leaves 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, clear sentence that efficiently communicates the tool's purpose without unnecessary words. It's appropriately sized and front-loaded, making it easy to understand at a glance.
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?
For a search tool with no annotations and no output schema, the description is incomplete. It doesn't explain what kind of results to expect (e.g., links, summaries), how many results are returned, or any behavioral nuances. This leaves the agent with insufficient 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?
The input schema has 100% description coverage, with the single parameter 'query' documented as 'Search query'. The description doesn't add any additional meaning beyond this, such as query syntax examples or search scope details. Given the high schema coverage, 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 clearly states the action ('Search') and target resource ('MDN Web Docs for web development documentation'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this tool from its sibling tools (search_all, search_github, etc.) beyond specifying the MDN source, which prevents a perfect score.
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 alternatives. There's no mention of specific use cases for MDN documentation over other sources like GitHub or Stack Overflow, nor any prerequisites or exclusions for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search npm registry for JavaScript packages
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query | |
| limit | No | Maximum number of results (default: 5) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure but offers minimal information. It doesn't mention whether this is a read-only operation, what authentication might be required, rate limits, network behavior, or what format results will be returned in. For a search tool with zero annotation coverage, this represents a significant transparency gap.
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 extremely concise at just one sentence with zero wasted words. It's front-loaded with the core purpose and contains no unnecessary information. Every word earns its place in this minimal but complete statement of function.
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?
For a search tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what information will be returned, how results are structured, whether there's pagination, or any behavioral characteristics. The agent would need to guess about the tool's operation and output format based solely on this brief description.
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?
The description adds no parameter-specific information beyond what's already in the schema, which has 100% coverage with clear descriptions for both 'query' and 'limit' parameters. Since the schema does the heavy lifting, the baseline score of 3 is appropriate - the description doesn't add value but doesn't need to compensate for schema deficiencies either.
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: searching the npm registry for JavaScript packages. It specifies both the action ('search') and the target resource ('npm registry for JavaScript packages'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like search_pypi or search_github, which is why it doesn't earn a perfect score.
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. With sibling tools like search_pypi, search_github, and search_all available, there's no indication of when npm-specific searching is appropriate or when other search tools might be better suited. This lack of comparative context leaves the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search PyPI for Python packages
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Search' implies a read-only operation, it doesn't specify whether this requires authentication, rate limits, pagination behavior, or what the response format looks like. For a search tool with zero annotation coverage, this leaves significant behavioral gaps.
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 that states exactly what the tool does without any unnecessary words. It's appropriately sized and front-loaded with the core functionality, making it easy for an agent to parse quickly.
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 low complexity (single parameter, no output schema, no annotations), the description is minimally adequate but lacks important context. It doesn't explain what the search returns (packages, versions, metadata), how results are formatted, or any limitations. While the schema covers the parameter, the absence of output information and behavioral details makes this incomplete for optimal agent 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?
The input schema has 100% description coverage, with the single 'query' parameter documented as 'Search query.' The description doesn't add any additional meaning about parameter usage, syntax, or examples beyond what the schema provides. According to the scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no parameter information in the description.
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 ('Search') and target resource ('PyPI for Python packages'), making the purpose immediately understandable. However, it doesn't differentiate this tool from its sibling search tools (search_github, search_npm, etc.) beyond specifying the PyPI platform, which is why it doesn't reach a perfect 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 like search_all, search_github, or search_npm. It doesn't mention any specific contexts, prerequisites, or exclusions for using this PyPI-specific search tool, leaving the agent without usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search Stack Overflow for programming questions and answers
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query | |
| limit | No | Maximum number of results (default: 5) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It states the basic function but doesn't reveal important traits like whether this is a read-only operation, rate limits, authentication requirements, pagination behavior, or what format results return. For a search tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.
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 that states the core function without unnecessary words. It's appropriately sized for a simple search tool and front-loads the essential information, making it easy for an agent to parse quickly.
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 no annotations and no output schema, the description should provide more context about behavioral aspects and result format. While the purpose is clear, important details like whether this is a safe read operation, what the results look like, or any limitations are missing, making it incomplete for effective tool selection and invocation.
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 fully documents both parameters (query and limit). The description doesn't add any parameter-specific information beyond what's in the schema, such as query syntax examples or result format details. The baseline score of 3 reflects adequate but minimal value addition.
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 ('Search') and target resource ('Stack Overflow for programming questions and answers'), making the purpose immediately understandable. However, it doesn't specifically differentiate from sibling tools like search_all or search_github, which likely search different platforms for similar content.
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 like search_all (which might include Stack Overflow) or other platform-specific search tools. There's no mention of use cases, prerequisites, or exclusions that would help an agent choose appropriately among the available search options.
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.
search_allsearch_githubsearch_mdnsearch_npmsearch_pypisearch_stackoverflowScored across 6 tools
Each tool has a clearly distinct purpose targeting a specific platform or resource, with no overlap in functionality. The descriptions explicitly differentiate the search targets (e.g., GitHub repositories, MDN documentation, npm packages), making it easy for an agent to select the appropriate tool without confusion.
All tool names follow a consistent verb_noun pattern with 'search_' as the prefix, followed by the platform name (e.g., search_github, search_mdn). This predictable naming scheme enhances readability and agent usability, with no deviations or mixed conventions.
With 6 tools, the server is well-scoped for its purpose of code research across multiple platforms. Each tool earns its place by covering a distinct and relevant source (e.g., GitHub, MDN, npm, PyPI, Stack Overflow), avoiding both thinness and bloat for this domain.
The tool surface is complete for the server's stated purpose of code research, covering major platforms developers commonly use (GitHub, MDN, npm, PyPI, Stack Overflow) and including a unified search_all tool. There are no obvious gaps in coverage that would cause agent failures for typical research tasks.
Search GitHub, npm, PyPI, StackOverflow, ArXiv from one MCP — built for coding agents.
Find open-source libraries and fetch contextual code snippets by version to accelerate development…
Search public open-source code, documentation, metadata, vulnerabilities, changelogs, and examples.
Finds real, maintained open-source repos that fit your project. MCP grounding for coding agents.
