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this should be a MCP server the agent can use and optimize on I have a MCP server that wraps developer tool CLIs (linting, tests, etc), but this would need a t

this should be a MCP server the agent can use and optimize on I have a MCP server that wraps developer tool CLIs (linting, tests, etc), but this would need a textual report instead of HTML. https://github.com/scosman/hooks_mcp https://github.com/scosman/hooks_mcp

brynary 1y ago
What benefits do you see from having the agent call a CLI like this via MCP as opposed to just executing the CLI as a shell command and taking action on the stdout?
- mogwire 1y ago
  This is one of the most important questions I see when people recommend an MCP server. If cursor and Claude code can already run an executable why do I need to add an MCP server in front of it? I feel like a lot of times it’s, “Because AI”
- scosman 1y ago
  Few things: - Security/Speed: I leave "approve CLI commands" on in Cursor. This functions as a whitelist of known safe commands. It only needs to ask if running a non-standard command, 99% of the time it can use tools. It will also verify paths passed by the model are in the project folder (not letting it execute on external files) - Discoverability: For agents to work well, you need to explain which commands are available, when to use each, parameters, etc. This is a more formal version than a simple AGENTS.md, with typed parameters, tool descriptions, etc. - Correctness: I find models mess up command strings or run them in the wrong folders. This is more robust than pure strings, with short tool names, type checking, schemas, etc. - Parallel execution: MCP tools can run in parallel, CLI tools typically can't - Sharing across team: which dev commands to run can be spread across agents.md, github workflows, etc. This is one central place for the agents use case. - Prompts: MCP also supports prompts (less known MCP feature). Not really relevant to the "why not CLI" question, but it's a benefit of the tool. It provides a short description of the available prompts, then lets the model load any by name. It's requires much less room in context than loading an entire /agents folder.
ano-ther 1y ago
It has a JSON option, would that work? pyscn analyze --json . # Generate JSON report
- scosman 1y ago
  it would!
daydreamer9000 1y ago
Coding agents usually have longer timeouts for bash commands than MCP server calls. So depending on how much time this tool takes, using a CLI by default could be more robust.
d-yoda 1y ago
MCP integration could be a good option. If there's interest, feel free to raise an issue on GitHub.

brynary 1y ago

What benefits do you see from having the agent call a CLI like this via MCP as opposed to just executing the CLI as a shell command and taking action on the stdout?

mogwire 1y ago
This is one of the most important questions I see when people recommend an MCP server. If cursor and Claude code can already run an executable why do I need to add an MCP server in front of it? I feel like a lot of times it’s, “Because AI”
scosman 1y ago
Few things: - Security/Speed: I leave "approve CLI commands" on in Cursor. This functions as a whitelist of known safe commands. It only needs to ask if running a non-standard command, 99% of the time it can use tools. It will also verify paths passed by the model are in the project folder (not letting it execute on external files) - Discoverability: For agents to work well, you need to explain which commands are available, when to use each, parameters, etc. This is a more formal version than a simple AGENTS.md, with typed parameters, tool descriptions, etc. - Correctness: I find models mess up command strings or run them in the wrong folders. This is more robust than pure strings, with short tool names, type checking, schemas, etc. - Parallel execution: MCP tools can run in parallel, CLI tools typically can't - Sharing across team: which dev commands to run can be spread across agents.md, github workflows, etc. This is one central place for the agents use case. - Prompts: MCP also supports prompts (less known MCP feature). Not really relevant to the "why not CLI" question, but it's a benefit of the tool. It provides a short description of the available prompts, then lets the model load any by name. It's requires much less room in context than loading an entire /agents folder.

ano-ther 1y ago

It has a JSON option, would that work? pyscn analyze --json . # Generate JSON report

scosman 1y ago
it would!

daydreamer9000 1y ago

Coding agents usually have longer timeouts for bash commands than MCP server calls. So depending on how much time this tool takes, using a CLI by default could be more robust.

d-yoda 1y ago

MCP integration could be a good option. If there's interest, feel free to raise an issue on GitHub.