Tools: Code Is All You Need
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Tools: Code Is All You Need
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Re: Tools: Code Is All You Need
#2> try completing a GitHub task with the GitHub MCP, then repeat it with the gh CLI tool. You'll almost certainly find the latter uses context far more efficiently and you get to your intended results quicker.
This is spot on. I have a "devops" folder with a CLAUDE.md with bash commands for common tasks (e.g. find prod / staging logs with this integration ID).
When I complete a novel task (e.g. count all the rows that were synced from stripe to duckdb) I tell Claude to update CLAUDE.md with the example. The next time I ask a similar question, Claude one-shots it.
This is the first few lines of the CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
## Purpose
This devops folder is dedicated to Google Cloud Platform (GCP) operations, focusing on:
- Google Cloud Composer (Airflow) DAG management and monitoring
- Google Cloud Logging queries and analysis
- Kubernetes cluster management (GKE)
- Cloud Run service debugging
## Common DevOps Commands
### Google Cloud Composer
```bash
# View Composer environment details
gcloud composer environments describe meltano --location us-central1 --project definite-some-id
# List DAGs in the environment
gcloud composer environments storage dags list --environment meltano --location us-central1 --project definite-some-id
# View DAG runs
gcloud composer environments run meltano --location us-central1 dags list
# Check Airflow logs
gcloud logging read 'resource.type="cloud_composer_environment" AND resource.labels.environment_name="meltano"' --project definite-some-id --limit 50Re: Tools: Code Is All You Need
#3More appropriately: the terminal is all you need.
I have used MCP daily for a few months. I'm now down to a single MCP server: terminal (iTerm2). I have OpenAPI specs on hand if I ever need to provide them, but honestly shell commands and curl get you pretty damn far.
Re: Tools: Code Is All You Need
#4> try completing a GitHub task with the GitHub MCP, then repeat it with the gh CLI tool. You'll almost certainly find the latter uses context far more efficiently and you get to your intended results quicker. This is spot on. I have a "devops" folder with a CLAUDE.md with bash commands for common tasks (e.g. find prod / staging logs with this integration ID). When I complete a novel task (e.g. count all the rows that…
Just as a related aside, you could literally make that bottom section into a super simple stdio MCP Server and attach that to Claude Code. Each of your operations could be a tool and have a well-defined schema for parameters. Then you are giving the LLM a more structured and defined way to access your custom commands. I'm pretty positive there are even pre-made MCP Servers that are designed for just this activity.
Edit: First result when looking for such an MCP Server: https://github.com/inercia/MCPShell
Re: Tools: Code Is All You Need
#5Directionally I think this is right. Most LLM usage at scale tends to be filling the gaps between two hardened interfaces. The reliability comes not from the LLM inference and generation but the interfaces themselves only allowing certain configuration to work with them.
LLM output is often coerced back into something more deterministic such as types, or DB primary keys. The value of the LLM is determined by how well your existing code and tools model the data, logic, and actions of your domain.
In some ways I view LLMs today a bit like 3D printers, both in terms of hype and in terms of utility. They excel at quickly connecting parts similar to rapid prototyping with 3d printing parts. For reliability and scale you want either the LLM or an engineer to replace the printed/inferred connector with something durable and deterministic (metal/code) that is cheap and fast to run at scale.
Additionally, there was a minute during the 3D printer Gardner hype cycle where there were notions that we would all just print substantial amounts of consumer goods when the reality is the high utility use case are much more narrow. There is a corollary here to LLM usage. While LLMs are extremely useful we cannot rely on LLMs to generate or infer our entire operational reality or even engage meaningfully with it without some sort of pre-existing digital modeling as an anchor.
Re: Tools: Code Is All You Need
#6> This is a significant advantage that an MCP (Multi-Component Pipeline) typically cannot offer
Oh god please no, we must stop this initialism. We've gone too far.
Re: Tools: Code Is All You Need
#7> try completing a GitHub task with the GitHub MCP, then repeat it with the gh CLI tool. You'll almost certainly find the latter uses context far more efficiently and you get to your intended results quicker. This is spot on. I have a "devops" folder with a CLAUDE.md with bash commands for common tasks (e.g. find prod / staging logs with this integration ID). When I complete a novel task (e.g. count all the rows that…
Just as a related aside, you could literally make that bottom section into a super simple stdio MCP Server and attach that to Claude Code. Each of your operations could be a tool and have a well-defined schema for parameters. Then you are giving the LLM a more structured and defined way to access your custom commands. I'm pretty positive there are even pre-made MCP Servers that are designed for just this activity. Ed…
wouldn't this defeat the point? Claude Code already has access to the terminal, adding specific instruction in the context is enough
Re: Tools: Code Is All You Need
#8Isn't it a bit like saying: saw is all you need (for carpenters)?
I mean, you _probably_ could make most furniture with only a saw, but why?
Re: Tools: Code Is All You Need
#9More appropriately: the terminal is all you need. I have used MCP daily for a few months. I'm now down to a single MCP server: terminal (iTerm2). I have OpenAPI specs on hand if I ever need to provide them, but honestly shell commands and curl get you pretty damn far.
I never knew how far it was possible to go in bash shell with the built-in tools until I saw the LLMs use them.
Re: Tools: Code Is All You Need
#10Tools are constraints and time/token savers. Code is expensive in terms of tokens and harder to constrain in environments that can’t be fully locked-down because network access for example is needed by the task. You need code AND tools.