Un servidor de Protocolo de Contexto de Modelo que proporciona herramientas para buscar y acceder a recursos de programación en múltiples plataformas. Este servidor se integra con plataformas de desarrollo populares para ayudar a los LLM a encontrar ejemplos de código, documentación y paquetes relevantes.
Stack Overflow - Preguntas y respuestas sobre programación
MDN Web Docs: documentación de desarrollo web
GitHub - Búsqueda de código y repositorio
npm - Registro de paquetes de JavaScript
PyPI - Índice de paquetes de Python
search_stackoverflowBusque en Stack Overflow preguntas y respuestas de programación.
Parámetros:
query (obligatoria): cadena de consulta de búsqueda
limit (opcional): Máximo de resultados (1-10, predeterminado: 5)
Devuelve: Lista formateada de preguntas con puntuaciones, recuentos de respuestas y extractos
Los resultados se almacenan en caché durante 1 hora
search_mdnBusque documentación sobre desarrollo web en MDN Web Docs.
Parámetros:
query (obligatoria): cadena de consulta de búsqueda
Devoluciones: Las 5 principales coincidencias de documentación de MDN con resúmenes y enlaces
Los resultados se almacenan en caché durante 1 hora
search_githubBusque en GitHub repositorios y ejemplos de código.
Parámetros:
query (obligatoria): cadena de consulta de búsqueda
language (opcional): Filtrar por lenguaje de programación
limit (opcional): Máximo de resultados por categoría (1-10, predeterminado: 5)
Devoluciones: Dos secciones:
Los mejores repositorios ordenados por estrellas
Archivos de código relevantes con contexto de repositorio
Los resultados se almacenan en caché durante 1 hora
search_npmBusque paquetes de JavaScript en el registro npm.
Parámetros:
query (obligatoria): cadena de consulta de búsqueda
limit (opcional): Máximo de resultados (1-10, predeterminado: 5)
Devoluciones: Información del paquete, incluida la versión, la descripción y las estadísticas de descarga.
Los resultados se almacenan en caché durante 1 hora
search_pypiBusque paquetes de Python en PyPI.
Parámetros:
query (obligatoria): cadena de consulta de búsqueda
Devoluciones: Información detallada del paquete, incluyendo versión, autor y enlaces.
Los resultados se almacenan en caché durante 1 hora
search_allBusque en todas las plataformas simultáneamente para obtener resultados completos.
Parámetros:
query (obligatoria): cadena de consulta de búsqueda
limit (opcional): Máximo de resultados por plataforma (1-5, predeterminado: 3)
Devoluciones: Resultados combinados de todas las plataformas:
Preguntas y respuestas de Stack Overflow
Documentación de MDN
Repositorios y código de GitHub
paquetes npm
Paquetes de PyPI
Los resultados se almacenan en caché durante 1 hora
Nota: ejecuta todas las búsquedas en paralelo para una respuesta más rápida
Related MCP server: Context7 MCP Clone
Node.js >= 20.11.0
npm >= 10.0.0
Opcional: Token de acceso personal de GitHub para límites de velocidad de API más altos
Para instalar Code Research Server para Claude Desktop automáticamente a través de Smithery :
npx -y @smithery/cli install @nahmanmate/code-research-mcp-server --client claudeClonar el repositorio e instalar las dependencias:
git clone https://github.com/nahmanmate/code-research-mcp-server.git
cd code-research-server
npm installConstruir el servidor:
npm run buildConfigurar los ajustes de MCP:
Agregue la configuración del servidor a su archivo de configuración de MCP:
VSCode: ~/.vscode-server/data/User/globalStorage/rooveterinaryinc.roo-cline/settings/cline_mcp_settings.json
Escritorio de Claude:
MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Ventanas: %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": []
}
}
}Nota: Reemplace /absolute/path/to con la ruta real donde clonó el repositorio.
Para desarrollo con reconstrucción automática ante cambios:
npm run watchEl servidor implementa un manejo robusto de errores:
Mensajes de error específicos de la API para cada plataforma
Manejo del límite de velocidad para la API de GitHub
Respaldos elegantes en caso de indisponibilidad del servicio
Respuestas almacenadas en caché para reducir la carga de la API
Dado que los servidores MCP se comunican a través de stdio, la depuración puede ser complicada. Utilice el Inspector MCP para una monitorización detallada de solicitudes y respuestas:
npm run inspectorEl Inspector proporciona:
Monitoreo de solicitudes y respuestas en tiempo real
Seguimiento de la ejecución de herramientas
Rastreos de pila de errores
Métricas de rendimiento
Visite la URL proporcionada en su navegador para acceder a la interfaz de depuración.
Los resultados se almacenan en caché mediante node-cache :
TTL predeterminado: 1 hora
Claves de caché independientes por combinación de consulta/límite
Estrategias de almacenamiento en caché específicas de la plataforma
Almacenamiento con uso eficiente de la memoria
Bifurcar el repositorio
Crear una rama de características
Confirme sus cambios
Empujar hacia la rama
Crear una solicitud de extracción
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. Dates show when Glama detected each change.
search_allsearch_githubsearch_mdnsearch_npmsearch_pypisearch_stackoverflowEach 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.
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If you are the server author, to access and configure the admin panel.
Search GitHub, npm, PyPI, StackOverflow, ArXiv from one MCP — built for coding agents.
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