MCP Issue
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Issuecreate an issue from the staged changes in the current branch"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Issue - GitHub Issue Creator from Git Diff
Um servidor MCP (Model Context Protocol) que permite criar issues no GitHub automaticamente a partir de diffs do Git. Desenvolvido por Gustavo Detoni.
Sobre o Projeto
Este projeto é um servidor MCP que oferece ferramentas para:
Gerar diffs do Git (working directory, staged changes ou range de commits)
Criar prompts estruturados para gerar issues a partir de diffs
Criar issues no GitHub automaticamente usando a API do GitHub
Related MCP server: Agentic MCP Server
Funcionalidades
Ferramentas Disponíveis:
git_diff- Gera diffs do repositório GitSuporte para diferentes modos: working, staged, range
Filtros por arquivos específicos
Controle de tamanho máximo do output
create_github_issue- Cria issues no GitHubIntegração com a API do GitHub via Octokit
Suporte a labels e assignees
Configuração de repositório padrão
Prompts Disponíveis:
draft_issue_from_diff- Converte diffs em rascunhos de issues estruturadasTemplate padronizado com seções: Summary, Context, Observations, etc.
Detecção automática do tipo de mudança (bug/feat/chore/refactor)
Sugestão de labels apropriadas
Instalação
Pré-requisitos
Node.js (versão 18+)
pnpm
Git configurado no sistema
Token do GitHub com permissões para criar issues
Passos de Instalação
Clone o repositório:
git clone <url-do-repositorio>
cd mcp-issueInstale as dependências:
pnpm installConfigure as variáveis de ambiente:
cp .env.example .envEdite o arquivo .env com suas configurações:
GITHUB_TOKEN=seu_token_do_github
DEFAULT_OWNER=seu_usuario_ou_organizacao
DEFAULT_REPO=nome_do_repositorio_padraoCompile o projeto:
pnpm run build⚙️ Configuração no Claude Desktop
Para usar este servidor MCP com o Claude Desktop, adicione a seguinte configuração no arquivo de configuração do Claude:
{
"mcpServers": {
"boxtime-mcp": {
"command": "node",
"args": [
"C:\\Users\\gusta\\Desktop\\projects\\mcp-issue\\dist\\index.js"
],
"env": {
"GITHUB_TOKEN": "seu_token_aqui",
"DEFAULT_OWNER": "seu_usuario",
"DEFAULT_REPO": "seu_repositorio"
}
}
}
}Importante: Substitua o caminho no array args pelo caminho absoluto correto para o arquivo dist/index.js no seu sistema.
Scripts Disponíveis
pnpm run build- Compila o TypeScriptpnpm run dev- Executa em modo desenvolvimentopnpm run start- Executa a versão compilada
Configuração do Token GitHub
Para obter um token do GitHub:
Acesse GitHub → Settings → Developer settings → Personal access tokens
Gere um novo token com as permissões:
repo(para repositórios privados)public_repo(para repositórios públicos)issues(para criar e gerenciar issues)
Template de Issue
O projeto usa um template estruturado para issues com as seguintes seções:
Summary: Resumo conciso da implementação
Context: Contexto da mudança ou problema resolvido
Observations: Pontos importantes da implementação
Acceptance Criteria: Critérios de aceitação baseados no diff
Action Plan: Ações/tarefas implementadas
Dependencies/Risks: Dependências e riscos identificados
Technical Notes: Detalhes técnicos relevantes
Contribuição
Contribuições são bem-vindas! Sinta-se à vontade para abrir issues ou pull requests.
Available Tools
2 toolscreate_github_issueCreate GitHub IssueC
Cria uma issue no GitHub usando Octokit
| Name | Required | Description | Default |
|---|---|---|---|
| assignees | No | ||
| body | No | ||
| labels | No | ||
| owner | No | ||
| repo | No | ||
| title | Yes |
TDQS
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 tool creates an issue (implying a write/mutation operation) but doesn't mention authentication requirements, rate limits, error conditions, or what happens on success/failure. For a mutation tool with zero 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 a single sentence that directly states the tool's function. It's appropriately brief and front-loaded with the core purpose. While it could be more informative, it doesn't waste words or include unnecessary 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?
For a mutation tool with 6 parameters, 0% schema coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain parameter meanings, behavioral expectations, or return values. The agent would struggle to use this tool correctly without additional context or trial-and-error.
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?
With 0% schema description coverage and 6 parameters (5 with defaults, 1 required), the description provides no information about any parameters. It doesn't explain what 'owner', 'repo', 'title', 'body', 'labels', or 'assignees' mean or how they should be formatted. The description fails to compensate for the complete lack of schema documentation.
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 states the tool's purpose ('Cria uma issue no GitHub') which translates to 'Creates an issue on GitHub', providing a clear verb+resource. However, it doesn't differentiate from the sibling tool 'git_diff' or specify what makes this tool unique. The mention of 'usando Octokit' adds implementation detail but doesn't enhance functional distinction.
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. There's no mention of prerequisites, when-not-to-use scenarios, or comparison with the sibling tool 'git_diff'. The agent must infer usage from 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.
git_diffGet Git diffB
Retorna um diff do repositório atual. Use para entender alterações locais.
| Name | Required | Description | Default |
|---|---|---|---|
| base | No | ||
| head | No | ||
| maxBytes | No | ||
| mode | No | working | |
| pathSpec | No |
TDQS
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 mentions the tool returns a diff for understanding local changes, but lacks details on behavioral traits such as whether it requires specific Git states, how it handles errors, if it's read-only or has side effects, or any rate limits. This is a significant gap for a tool with 5 parameters and no annotation coverage.
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 appropriately sized and front-loaded with the core purpose in the first sentence, followed by a brief usage note. Both sentences earn their place by providing essential information without redundancy or unnecessary details, making it highly efficient 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 (5 parameters, 0% schema coverage, no annotations, no output schema), the description is incomplete. It doesn't explain the return values, parameter usage, or behavioral context needed for effective tool invocation. The description should do more to compensate for the lack of structured data, especially for a tool with multiple parameters and no output schema.
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 schema description coverage is 0%, meaning none of the 5 parameters are documented in the schema. The description does not mention any parameters or add meaning beyond the schema, failing to compensate for the low coverage. This leaves the agent with no guidance on what parameters like 'base', 'head', 'mode', etc., mean or how to use them effectively.
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: 'Retorna um diff do repositório atual' (Returns a diff of the current repository). It specifies the verb (returns) and resource (diff of current repository), making the function unambiguous. However, it doesn't explicitly differentiate from the sibling tool 'create_github_issue', which serves a completely different purpose, so it doesn't fully earn a 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 implied usage guidance: 'Use para entender alterações locais' (Use to understand local changes). This suggests when to use the tool (for understanding local changes) but doesn't explicitly state when not to use it or mention alternatives. There's no comparison with the sibling tool or other potential tools, leaving some ambiguity in tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
v1.0.0- First observed
create_github_issue - First observed
git_diff
TDQS
Scored across 2 tools
The two tools have completely distinct purposes: one creates GitHub issues via Octokit, while the other returns a diff of the current repository. There is no overlap in functionality or ambiguity between them.
Both tools use snake_case naming, which is consistent. However, one tool includes 'github' in its name while the other does not, creating a minor deviation in naming pattern. The verbs 'create' and 'git_diff' (which is more of a noun phrase) are not perfectly aligned in style.
With only 2 tools, the server feels thin for a domain that appears to involve GitHub and repository operations. This limited set does not provide comprehensive coverage for typical workflows, such as reading or updating issues, managing repositories, or handling other common tasks.
The tool set is severely incomplete for a server named 'MCP Issue' and including GitHub-related functionality. There are significant gaps: no tools to list, get, update, or delete issues, and no broader GitHub operations like managing pull requests or repositories. This will likely cause agent failures in many scenarios.
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