SushiMCP
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., "@SushiMCP add context from my local API for better code generation"
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.
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SushiMCP
SushiMCP is a model context protocol server designed to assist developers with delivering context to their AI IDE's. It's simple to use and massively improves the performance of base and premium LLM models when generating code. The easiest way to get started is by registering SushiMCP with your client using the default configuration:
Registering SushiMCP with an MCP Client
{
"sushimcp": {
"command": "npx",
"args": [
"-y",
"@chriswhiterocks/sushimcp@latest",
"--llms-txt-source",
"cool_project:https://coolproject.dev/llms-full.txt",
"--openapi-spec-source",
"local_api:http://localhost:8787/api/v1/openapi.json"
]
}
}Related MCP server: @lex-tools/codebase-context-dumper
Advanced Configuration & Deeper Learning
Visit the SushiMCP Docs for more information on advanced configuration and deeper learning about SushiMCP.
Glama.ai Ratings
Author
Chris White: Email | GitHub | Discord | Personal Site | X | LinkedIn | Five9 Cyber
License
This project is licensed under the AGPL-3.0-or-later. See the license.txt file for details.
Available Tools
4 toolsfetch_llms_txtB
Fetches the content of one or more llms.txt URLs. Some llms.txt files compile a list of urls to other llms.txt file locations because listing their full documentation would bloat context. If the documentation you're looking for does not exist in the llms.txt, look for reference links to other llms.txt files and follow those.
Inputs: input (required).
fetch_openapi_specC
Fetches the content of one or more OpenAPI spec URLs.
Inputs: input (required).
list_llms_txt_sourcesA
This tool lists all available source urls where an llms.txt can be fetched. After reading the listed sources, use fetch_llms_txt to fetch any source that matches a technology in the instructions you received. Prefer llms.txt, but if llms.txt proves inadequate, check to see if other llms-full.txt or llms-mini.txt exist. When done, ask the user if they want to use other tools to search for documentation on any sources this tool could not find.
Inputs: no named parameters.
list_openapi_spec_sourcesB
This tool lists all available source urls where an OpenAPI spec can be fetched.
Inputs: no named parameters.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose: fetch_llms_txt and fetch_openapi_spec retrieve content from URLs, while list_llms_txt_sources and list_openapi_spec_sources list available sources for those URLs. There is no overlap in functionality, and the descriptions clearly differentiate between fetching and listing operations for two distinct resource types (llms.txt and OpenAPI specs).
All tool names follow a consistent verb_noun pattern with snake_case: fetch_llms_txt, fetch_openapi_spec, list_llms_txt_sources, and list_openapi_spec_sources. The naming is predictable and readable, with 'fetch' for retrieval actions and 'list' for source enumeration, maintaining uniformity throughout the set.
With 4 tools, the count is reasonable for a server focused on fetching and listing documentation sources. It covers two resource types (llms.txt and OpenAPI specs) with complementary operations, but it might feel slightly thin if expanded to handle more documentation formats or additional actions like validation or processing. Overall, it's well-scoped for its apparent purpose.
The tool set provides basic fetch and list operations for llms.txt and OpenAPI specs, covering retrieval and source discovery. However, there are notable gaps: no tools for updating, deleting, or validating these resources, and no operations for other documentation formats mentioned in descriptions (e.g., llms-full.txt or llms-mini.txt). This limits the surface to read-only actions, which may cause agents to hit dead ends in more complex workflows.
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