Most of the conversation around MCP is about what it enables. If you flip it - and ask what agents actually need to run efficiently - the math looks pretty broken.

基于 FastMCP 的本地 MCP 服务器，把 𝕏 API 的 OpenAPI 规范自动转化为 MCP 工具。

A connector solves this by packaging an integration into a single, reusable entity using the MCP protocol.

Most skills require you to install a dedicated CLI. But what if you aren't in a local terminal? ChatGPT can't run CLIs. Neither can Perplexity or the standard web version of Claude.

When a remote MCP server is updated with new tools or resources, every client instantly gets the latest version. No need to push updates, upgrade packages, or reinstall binaries.

For remote MCP servers, you don't need to install anything locally. You just point your client to the MCP server URL, and it works.

MCP was donated to the new Agentic AI Foundation at the start of December. Skills were promoted to an “open format” on December 18th.

Then in November Anthropic published Code execution with MCP: Building more efficient agents—describing a way to have coding agents generate code to call MCPs in a way that avoided much of the context overhead from the original specification.

Anthropic themselves appeared to acknowledge this later in the year with their release of the brilliant Skills mechanism—see my October post Claude Skills are awesome, maybe a bigger deal than MCP. MCP involves web servers and complex JSON payloads. A Skill is a Markdown file in a folder, optionally accompanied by some executable scripts.

The reason I think MCP may be a one-year wonder is the stratospheric growth of coding agents. It appears that the best possible tool for any situation is Bash—if your agent can run arbitrary shell commands, it can do anything that can be done by typing commands into a terminal. Since leaning heavily into Claude Code and friends myself I’ve hardly used MCP at all—I’ve found CLI tools like gh and libraries like Playwright to be better alternatives to the GitHub and Playwright MCPs.

f you define agents as LLM systems that can perform useful work via tool calls over multiple steps then agents are here and they are proving to be extraordinarily useful. The two breakout categories for agents have been for coding and for search.

It turned out that the real unlock of reasoning was in driving tools. Reasoning models with access to tools can plan out multi-step tasks, execute on them and continue to reason about the results such that they can update their plans to better achieve the desired goal. A notable result is that AI assisted search actually works now. Hooking up search engines to LLMs had questionable results before, but now I find even my more complex research questions can often be answered by GPT-5 Thinking in ChatGPT. Reasoning models are also exceptional at producing and debugging code. The reasoning trick means they can start with an error and step through many different layers of the codebase to find the root cause. I’ve found even the gnarliest of bugs can be diagnosed by a good reasoner with the ability to read and execute code against even large and complex codebases.

some practices that can make those discussions easier, by starting with constraints that even skeptical developers can see the value in: Build tools around verbs, not nouns. Create checkEligibility() or getRecentTickets() instead of getCustomer(). Verbs force you to think about specific actions and naturally limit scope.Talk about minimizing data needs. Before anyone creates an MCP tool, have a discussion about what the smallest piece of data they need to provide for the AI to do its job is and what experiments they can run to figure out what the AI truly needs.Break reads apart from reasoning. Separate data fetching from decision-making when you design your MCP tools. A simple findCustomerId() tool that returns just an ID uses minimal tokens—and might not even need to be an MCP tool at all, if a simple API call will do. Then getCustomerDetailsForRefund(id) pulls only the specific fields needed for that decision. This pattern keeps context focused and makes it obvious when someone’s trying to fetch everything.Dashboard the waste. The best argument against data hoarding is showing the waste. Track the ratio of tokens fetched versus tokens used and display them in an “information radiator” style dashboard that everyone can see. When a tool pulls 5,000 tokens but the AI only references 200 in its answer, everyone can see the problem. Once developers see they’re paying for tokens they never use, they get very interested in fixing it.

In an extreme case of data hoarding infecting an entire company, you might discover that every team in your organization is building their own blob. Support has one version of customer data, sales has another, product has a third. The same customer looks completely different depending on which AI assistant you ask. New teams come along, see what appears to be working, and copy the pattern. Now you’ve got data hoarding as organizational culture.

data hoarding trap find themselves violating the principle of least privilege: Applications should have access to the data they need, but no more

There’s also a security dimension to data hoarding that teams often miss. Every piece of data you expose through an MCP tool is a potential vulnerability. If an attacker finds an unprotected endpoint, they can pull everything that tool provides. If you’re hoarding data, that’s your entire customer database instead of just the three fields actually needed for the task.

MCP can remove the friction that comes from those trade-offs by letting us avoid having to make those decisions at all.

The team ended up with a data architecture that buried the signal in noise. That additional load put stress on the AI to dig out that signal, leading to serious potential long-term problems. But they didn’t realize it yet, because the AI kept producing reasonable-looking answers. As they added more data sources over the following weeks, the AI started taking longer to respond. Hallucinations crept in that they couldn’t track down to any specific data source. What had been a really valuable tool became a bear to maintain.

I’ve been watching teams adopt MCP over the past year, and I’m seeing a disturbing pattern. Developers are using MCP to quickly connect their AI assistants to every data source they can find—customer databases, support tickets, internal APIs, document stores—and dumping it all into the AI’s context.

Configure the Extension Install this extension from the VS Code marketplace Open VS Code Settings (Cmd/Ctrl + ,) Search for "Obsidian MCP"

The real power of MCP emerges when multiple servers work together, combining their specialized capabilities through a unified interface.

Prompts are structured templates that define expected inputs and interaction patterns. They are user-controlled, requiring explicit invocation rather than automatic triggering. Prompts can be context-aware, referencing available resources and tools to create comprehensive workflows. Similar to resources, prompts support parameter completion to help users discover valid argument values.

Prompts Prompts provide reusable templates. They allow MCP server authors to provide parameterized prompts for a domain, or showcase how to best use the MCP server. ​

Resources support two discovery patterns: Direct Resources - fixed URIs that point to specific data. Example: calendar://events/2024 - returns calendar availability for 2024 Resource Templates - dynamic URIs with parameters for flexible queries. Example: travel://activities/{city}/{category} - returns activities by city and category travel://activities/barcelona/museums - returns all museums in Barcelona Resource Templates include metadata such as title, description, and expected MIME type, making them discoverable and self-documenting.

Resources expose data from files, APIs, databases, or any other source that an AI needs to understand context. Applications can access this information directly and decide how to use it - whether that’s selecting relevant portions, searching with embeddings, or passing it all to the model.

Each tool performs a single operation with clearly defined inputs and outputs. Tools may require user consent prior to execution, helping to ensure users maintain control over actions taken by a model.

Tools are model-controlled, meaning AI models can discover and invoke them automatically. However, MCP emphasizes human oversight through several mechanisms. For trust and safety, applications can implement user control through various mechanisms, such as: Displaying available tools in the UI, enabling users to define whether a tool should be made available in specific interactions Approval dialogs for individual tool executions Permission settings for pre-approving certain safe operations Activity logs that show all tool executions with their results

Servers provide functionality through three building blocks:

Visual Studio Code acts as an MCP host. When Visual Studio Code establishes a connection to an MCP server, such as the Sentry MCP server, the Visual Studio Code runtime instantiates an MCP client object that maintains the connection to the Sentry MCP server.

The key participants in the MCP architecture are: MCP Host: The AI application that coordinates and manages one or multiple MCP clients MCP Client: A component that maintains a connection to an MCP server and obtains context from an MCP server for the MCP host to use MCP Server: A program that provides context to MCP clients

I use obsidian vault and also obsidian MCP server from chat client.With vscode I could use MCP to get content into the vault more easily, but refactoring notes, obsidian is better ux

we didn’t need MCP at all. That’s because MCP isn’t a fundamental enabling technology. The amount of coverage it gets is frustrating.

Using MCP, AI applications like Claude or ChatGPT can connect to data sources (e.g. local files, databases), tools (e.g. search engines, calculators) and workflows (e.g. specialized prompts)

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