[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

[athrowaway3z]

Yeah this is just straight up nonsense.

Its ability to shuffle around data and use bash and do so in interesting ways far outstrips its ability to deal with MCPs.

Also remember to properly name your cli tools and add a `use <mytool> --help for doing x` in your AGENTS.md, but that is all you need.

Maybe you're stuck on some bloated frontend harness?

[ianm218]

> Yeah this is just straight up nonsense.

I was just sharing my experience I'm not sure what you mean. Just n=1 data point.

From first principles I 100% agree and yes I was using a CLI tool I made with typer that has super clear --help + had documentation that was supposed to guide multi step workflows. I just got much better performance when I tried MCP. I asked Claude Code to explain the diff:

> why does our MCP onbaroding get better performance than the using objapi in order to make these pipelines? Like I can see the performance is better but it doesn't intuitively make sense to me why an mcp does better than an API for the "create a pipeline" workflow

It's not MCP-the-protocol vs API-the-protocol. They hit the same backend. The difference is who the interface was designed for.

  The CLI is a human interface that Claude happens to use. Every objapi pb call means:
  - Spawning a new Python process (imports, config load, HTTP setup)
  - Constructing a shell command string (escaping SQL in shell args is brutal)
  - Parsing Rich-formatted table output back into structured data
  - Running 5-10 separate commands to piece together the current state (conn list, sync list, schema classes, etc.)

  The MCP server is an LLM interface by design. The wins are specific:

  1. onboard://workspace-state resource — one call gives Claude the full picture: connections, syncs, object classes, relations, what exists, what's missing. With the CLI, Claude
  runs a half-dozen commands and mentally joins the output.
  2. Bundled operations — explore_connection returns tables AND their columns, PKs, FKs in one response. The CLI equivalent is conn tables → pick table → conn preview for each. Fewer
   round-trips = fewer places for the LLM to lose the thread.
  3. Structured in, structured out — MCP tools take JSON params, return JSON. No shell escaping, no parsing human-formatted tables. When Claude needs to pass a SQL string with quotes
   and newlines through objapi pb node add sql --sql "...", things break in creative ways.
  4. Tool descriptions as documentation — the MCP tool descriptions are written to teach an LLM the workflow. The CLI --help is written for humans who already know the concepts.
  5. Persistent connection — the MCP server keeps one ObjectsClient alive across all calls. The CLI boots a new Python process per command.

  So the answer is: same API underneath, but the MCP server eliminates the shell-string-parsing impedance mismatch and gives Claude the right abstractions (fewer, chunkier operations
   with full context) instead of making it pretend to be a human at a terminal.

For context I was working on a visual data pipeline builder and was giving it the same API that is used in the frontend - it was doing very poorly with the API.

[eli]

I have never had a problem using cli tools intead of mcp. If you add a little list of the available tools to the context it's nearly the same thing, though with added benefits of e.g. being able to chain multiple together in one tool call

[ianm218]

Not doubting you just sharing my experience - was able to get dramatically better experience for multi step workflows that involve feedback from SQL compilers with MCP. Probably the right harness to get the same performance with the right tools around the API calls but was easier to stop fighting it for me

[vidarh]

Did you test actually having command line tools that give you the same interface as the MCP's? Because that is what generally what people are recommending as the alternative. Not letting the agent grapple with <random tool> that is returning poorly structured data.

If you option is to have a "compileSQL" MCP tool, and a "compileSQL" CLI tool, that that both return the same data as JSON, the agent will know how to e.g. chain jq, head, grep to extract a subset from the latter in one step, but will need multiple steps with the MCP tool.

The effect compounds. E.g. let's say you have a "generateQuery" tool vs CLI. In the CLI case, you might get it piping the output from one through assorted operations and then straight into the other. I'm sure the agents will eventually support creating pipelines of MCP tools as well, but you can get those benefits today if you have the agents write CLI's instead of bothering with MCP servers.

I've for that matter had to replace MCP servers with scripts that Claude one-shot because the MCP servers lacked functionality... It's much more flexible.