11–20 of 194 posts
Moderne Ai agent tool have have a setting where you can trimm down the numbers of tools from an MCP server. Usefull to avoid overwhelming the LLM with 80 tools description when you only need 1
If there was some certainty MCP could add to this equation that would perhaps be theoretically nice, but otherwise it's just .. parsing, a perhaps not "solved" problem, but one for which there's already ample solutions.
"What is MCP, what does it bring to the table? Who knows. What does it do? The LLM stuff! Pay us $10 a month thanks!"
LLM's have function / tool calling built into them. No major models have any direct knowledge of MCP.
Not only do you not need MCP, but you should actively avoid using it.
Stick with tried and proven API standards that are actually observable and secure and let your models/agents directly interact with those API endpoints.
Moderne Ai agent tool have have a setting where you can trimm down the numbers of tools from an MCP server. Usefull to avoid overwhelming the LLM with 80 tools description when you only need 1
I don't find that to help much at all, particularly because some tools really only make sense with a bunch of other tools and then your context is already polluted. It's surprisingly hard to do this right, unless you have a single tool MCP (eg: a code/eval based tool, or an inference based tool).
Earlier quoted context omitted.
I don't find that to help much at all, particularly because some tools really only make sense with a bunch of other tools and then your context is already polluted. It's surprisingly hard to do this right, unless you have a single tool MCP (eg: a code/eval based tool, or an inference based tool).
Don't you have a post about writing Python instead of using MCP? I can't see how MCP is more efficient than giving the LLM a bunch of function signatures and allow it to call them, but maybe I'm not familiar enough with MCP.
Yes, and that works really well. I also tried various attempts of letting agents to write code that exposes MCP tool calls via an in-language API. But it's just really, really hard to work with because MCP tools are generally not in the training set, but normal APIs are.
Oh you're misunderstanding MCP here. MCP was created so llm companies can have a plugin system. So instead of them being the API provider, they can become the platform that we build apps/plugins for, and they become the user interface to end consumers.
what's the difference between that and those providers exposing an api?
The main issue is the disagreement on how to declare the MCP tool exists. Cursor, vscode, claude all use basically the same mcp.json file, but then codex uses `config.toml`. There's very little uniformity in project-specific MCP tools as well, they tend to be defined globally.
Earlier quoted context omitted.
Don't you have a post about writing Python instead of using MCP? I can't see how MCP is more efficient than giving the LLM a bunch of function signatures and allow it to call them, but maybe I'm not familiar enough with MCP.
> Don't you have a post about writing Python instead of using MCP? Yes, and that works really well. I also tried various attempts of letting agents to write code that exposes MCP tool calls via an in-language API. But it's just really, really hard to work with because MCP tools are generally not in the training set, but normal APIs are.
MCP was a really shitty attempt at building a plugin framework that was vague enough to lure people into and then allow other companies to build plugin platforms to take care of the MCP non-sense. "What is MCP, what does it bring to the table? Who knows. What does it do? The LLM stuff! Pay us $10 a month thanks!" LLM's have function / tool calling built into them. No major models have any direct knowledge of MCP. Not…
That's all it's doing. Just plain ole context pollution. World could be better served by continuing to build out the APIs that exist.