[_pdp_]

There is some important context missing from the article.

First, MCP tools are sent on every request. If you look at the notion MCP the search tool description is basically a mini tutorial. This is going right into the context window. Given that in most cases MCP tool loading is all or nothing (unless you pre-select the tools by some other means) MCP in general will bloat your context significantly. I think I counted about 20 tools in GitHub Copilot VSCode extension recently. That's a lot!

Second, MCP tools are not compossible. When I call the notion search tool I get a dump of whatever they decide to return which might be a lot. The model has no means to decide how much data to process. You normally get a JSON data dump with many token-unfriendly data-points like identifiers, urls, etc. The CLI-based approach on the other hand is scriptable. Coding assistant will typically pipe the tool in jq or tail to process the data chunk by chunk because this is how they are trained these days.

If you want to use MCP in your agent, you need to bring in the MCP model and all of its baggage which is a lot. You need to handle oauth, handle tool loading and selection, reloading, etc.

The simpler solution is to have a single MCP server handling all of the things at system level and then have a tiny CLI that can call into the tools.

In the case of mcpshim (which I posted in another comment) the CLI communicates with the sever via a very simple unix socket using simple json. In fact, it is so simple that you can create a bash client in 5 lines of code.

This method is practically universal because most AI agents these days know how to use SKILLs. So the goal is to have more CLI tools. But instead of writing CLI for every service you can simply pivot on top of their existing MCP.

This solves the context problem in a very elegant way in my opinion.

[tymscar]

So basically the best way to use MCP is not to use it at all and just call the APIs directly or through a CLI. If those dont exist then wrapping the MCP into a CLI is the second best thing.

Makes you wonder whats the point of MCP

[_pdp_]

The point of the MCP is for the upstream provider to provider agent specific tools and to handle authentication and session management.

Consider the Google Meet API. To get an actual transcript from Google Meet you need to perform 3-4 other calls before the actual transcript is retrieved. That is not only inefficient but also the agent will likely get it wrong at least once. If you have a dedicated MCP then Google in theory will provide a single transcript retrieval tool which simplifies the process.

The authentication story should not be underestimated either. For better or worse, MCP allows you to dynamically register oauth client through a self registration process. This means that you don't need to register your own client with every single provider. This simplifies oauth significantly. Not everyone supports it because in my opinion it is a security problem but many do.

[miroljub]

Exactly. You shouldn't use MCPs unless there is some statefulness / state / session they need to maintain between calls.

In all other cases, CLI or API calls are superior.

[jnstrdm05]

The guy who created fastmcp, he mentioned that you should use mcp to design how an llm should interact with the API, and give it tools that are geared towards solving problems, not just to interact with the API. Very interesting talk on the topic on YouTube. I still think it's a bloated solution.

[paulddraper]

MCP is just JSON-RPC plus dynamic OAuth plus some lifecycle things.

It’s a convention.

That everyone follows.

[throwup238]

> Makes you wonder whats the point of MCP

I only use them for stuff that needs to run in-process, like a QT MCP that gives agents access to the element hierarchy for debugging and interacting with the GUI (like giving it access to Chrome inspector but for QT).

[ianm218]

This was my initial understanding but if you want ai agents to do complex multi step workflows I.e. making data pipelines they just do so much better with MCP.

After I got the MCP working my case the performance difference was dramatic

[crazylogger]

Then you inevitably have to leak your API secret to the LLM in order for it to successfully call the APIs.

MCP is a thin toolcall auth layer that has to be there so that ChatGPT and claude.ai can "connect to your Slack", etc.

[brookst]

You’ve described a naive MCP implementation but it really doesn’t work that way IRL.

I have an MCP server with ~120 functions and probably 500k tokens worth of help and documentation that models download.

But not all at once, that would be crazy. A good MCP tool is hierarchical, with a very short intro, links to well-structured docs that the model can request small pieces of, groups of functions with `—-help` params that explain how to use each one, and agent-friendly hints for grouping often-sequential calls together.

It’s a similar optimization to what you’re talking about with CLI; I’d argue that transport doesn’t really matter.

There are bad MCP serves that dump 150k tokens of instructions at init, but that’s a bad implementation, not intrinsic to the interface.

[miki123211]

I'd add to that that every tool should have --json (and possibly --output-schema flags), where the latter returns a Typescript / Pydantic / whatever type definition, not a bloated, token-inefficient JSON schema. Information that those exist should be centralized in one place.

This way, agents can either choose to execute tools directly (bringing output into context), or to run them via a script (or just by piping to jq), which allows for precise arithmetic calculations and further context debloating.

[grogenaut]

Or write your own MCP server and make lots of little tools that activate on demand or put smarts or a second layer LLM into crafting GQL queries on the fly and reducing the results on the fly. They're kinda trivial to write now.

I do agree that MCP context management should be better. Amazon kiro took a stab at that with powers

[TCattd]

Another alternative (to mcpshim): https://github.com/EstebanForge/mcp-cli-ent

Direct usage as CLI tool.

[BeetleB]

> Given that in most cases MCP tool loading is all or nothing (unless you pre-select the tools by some other means)

Which applications that support MCP don't let you select the individual tools in a server?

[sakesun]

From your description, GraphQL or SQL could be a good solution for AI context as well.

[cjonas]

SQL is peak for data retrieval (obviously) but challenging to deploy for multitenant applications where you can't just give the user controlled agent a DB connection. I found it every effective to create a mini paquet "data ponds" on the fly in s3 and allow the agent to query it with duckdb (can be via tool call but better via a code interpreter). Nice thing with this approach is you can add data from any source and the agent can join efficiently.