mcpField
S
Sourcebot

Sourcebot

Local
github.com/sourcebot-devVersion Control4.0kUpdated today
Repository

About Sourcebot

Fetch code context from multiple code hosts (GitHub, GitLab, Bitbucket, etc)

#code-context#cursor#copilot

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "sourcebot-dev": {
      "command": "npx",
      "args": [
        "-y",
        "@sourcebot/mcp"
      ]
    }
  }
}
Run commandnpx -y @sourcebot/mcp

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npm@sourcebot/mcp

Tools

7

Saved tool list · updated 3w ago

ParameterTypeRequiredDescription
querystringYesThe search pattern to match against code contents. Do not escape quotes in your query.
useRegexbooleanNoWhether to use regular expression matching to match the search query against code contents. When false, substring matching is used. (default: false)
filterByReposarrayNoScope the search to the provided repositories.
filterByLanguagesarrayNoScope the search to the provided languages.
filterByFilepathsarrayNoScope the search to the provided filepaths.
caseSensitivebooleanNoWhether the search should be case sensitive (default: false).
includeCodeSnippetsbooleanNoWhether to include the code snippets in the response. If false, only the file's URL, repository, and language will be returned. (default: false)
refstringNoCommit SHA, branch or tag name to search on. If not provided, defaults to the default branch (usually 'main' or 'master').
maxTokensnumberNoThe maximum number of tokens to return (default: 10000). Higher values provide more context but consume more tokens. Values less than 10000 will be ignored.

Details

The maintainer has not added details yet. Check the README in the repository.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.