Reilly Wood

The Model Context Protocol (MCP) is a pretty big deal these days. It’s become the de facto standard for giving LLMs access to tools that someone else wrote, which, of course, turns them into agents. But writing tools for a new MCP server is hard, and so people often propose auto-converting existing APIs into MCP tools; typically using OpenAPI metadata (1, 2).

In my experience, this can work but it doesn’t work well. Here are a few reasons why:

Agents don’t do well with large numbers of tools

Infamously, VS Code has a hard limit of 128 tools - but many models struggle with accurate tool calling well before that number. Also, each tool and its description takes up valuable context window space.

Most web APIs weren’t designed with these constraints in mind! It’s fine to have umpteen APIs for a single product area when those APIs are called from code, but if each of those APIs is mapped to an MCP tool the results might not be great.

MCP tools designed from the ground up are typically much more flexible than individual web APIs, with each tool being able to do the work of several individual APIs.

APIs can blow through context windows quickly

Imagine an API that returns 100 records at a time, and each record is very wide (say, 50 fields). Sending those results to an agent as-is will use up a lot of tokens; even if a query can be satisfied with only a few fields, every field ends up in the context window.

APIs are typically paginated by the number of records, but records can vary a lot in size. One record might contain a large text field that takes up 100,000 tokens, while another might contain 10. Putting these API results directly into an agent’s context window is a gamble; sometimes it works, sometimes it will blow up.

The format of the data can also be an issue. Most web APIs these days return JSON, but JSON is a very token-inefficient format. Take this:

[
  {
    "firstName": "Alice",
    "lastName": "Johnson",
    "age": 28
  },
  {
    "firstName": "Bob",
    "lastName": "Smith",
    "age": 35
  }
]

Compare to the same data in CSV format:

firstName,lastName,age
Alice,Johnson,28
Bob,Smith,35

The CSV data is much more succinct - it uses up half as many tokens per record. Typically CSV, TSV, or YAML (for nested data) are better choices than JSON.

None these issues are insurmountable. You could imagine automatically adding tool arguments that let agents project fields, automatically truncating or summarizing large results, and automatically converting JSON results to CSV (or YAML for nested data). But the tools I’ve seen do none of those things.

APIs don’t make the most of agents’ unique capabilities

APIs return structured data for programmatic consumption. That’s often what agents want from tool calls… but agents can also handle other, more free-form instructions.

For example an ask_question tool could perform a RAG query over some documentation, then return information in plain text that is used to inform the next tool call - skipping structured data entirely.

Or, a call to a search_cities tool could return a structured list of cities and a suggestion of what to call next:

city_name,population,country,region
Tokyo,37194000,Japan,Asia
Delhi,32941000,India,Asia
Shanghai,28517000,China,Asia

Suggestion: To get more specific information (weather, attractions, demographics), try calling get_city_details with the city_name parameter.

That sort of layering and tool chaining can be very effective in MCP servers, and it’s something you’ll miss out on completely if auto-converting APIs to tools.

If an agent needs to call an API, it could just do that

Agents like Claude Code are remarkably capable of writing+executing code these days, including scripts that call web APIs. Some people take this so far as to argue that MCP isn’t needed at all!

I disagree with that conclusion, but I do think we should skate to where the puck is going. Sandboxing of agents is improving rapidly, and if it’s easy+safe for an agent to call APIs directly then we might as well do that and cut out the middleman.

Conclusion

Agents are fundamentally different from the typical consumers of APIs. It’s possible to automatically create MCP tools from existing APIs, but doing that is unlikely to work well. Agents do best when given tools that are designed for their unique capabilities and limitations.

I want to get something off my chest: attempts to keep the price of urban land down are not necessarily good. Many people in local politics place a high priority on keeping land prices down. For example, the new Vancouver councillor who opposed a church building apartments on their own land:

Land values displace people. This will increase land values.

…and then used that same reason to vote against apartments at a major train station:

I’m worried that filtering will take too long, that land value increases will lead to displacement…

Vancouver’s planning staff share these concerns and try to keep land values down when changing zoning. For example, the recent multiplex policy was designed to avoid raising land values:

Proposed density bonus contribution requirements & rates (are) set to… limit any potential land value escalation

That makes sense; if land value is lower then homes are more affordable, right?

WRONG (if you keep land values down by stopping development)

Land Prices Are Not Housing Prices

The main way people save on housing costs in cities is by using less land. For example, imagine the following uses on a 4000 sqft lot:

It is generally much cheaper to buy 800 square feet of land than it is to buy 4000. But where this gets interesting is that those denser uses may cause higher land prices. Let’s walk through how:

1. Say that 4000 sqft lot is zoned to only allow a single-family home. Richie McRicherson is willing to pay $1M so he can build a house on that land. The land sells for 1 MILLION DOLLARS.
2. Now, suppose the land is zoned to allow a duplex. 2 households who each have $600k pool their money together and outbid Richie. The land sells for 1.2 MILLION DOLLARS.
3. Finally, suppose the land is zoned to allow a 5-unit condo building. 5 households who each have $400k pool their money and outbid both Richie and the duplex buyers. The land sells for 2 MILLION DOLLARS.

It is really important to note that even though allowing more homes drove land prices up, households are paying less for land.

OK that’s the theory; what about in practice?

It can be hard to observe this in real life, because dense city centres tend to be pretty expensive. That’s a complicated topic that’s beyond the scope of this blog post, but there are places where it’s easy to see this specific phenomenon in Vancouver with a map of land values. For example:

North West Point Grey

This is one of the most expensive neighbourhoods in Vancouver, by design. Apartments are forbidden everywhere, only houses are allowed. And city planning rules require each house to use up much more land west of Blanca Street:

This is exactly what we were talking about. When the city lets people use less land per home, land prices go up and home prices go down. To be clear, $3M still isn’t cheap; we should go a lot further.

Shaughnessy

It’s a similar story in Shaughnessy, historically Vancouver’s most exclusive neighbourhood:

South of 16th we zone for mansions on very large lots (making the land relatively cheap), and north of 16th we allow apartments and condo buildings (making the land relatively expensive). If you know Vancouver at all, you know that those apartments are a lot cheaper than the $10M+ Shaughnessy mansions!

Takeaway

It’s important to distinguish between the cost of land per square foot and the cost of land per home. Limiting density does work to drive the former down, but at a terrible cost: it stops people of modest means from pooling their resources to outbid someone much richer.

I had an odd experience with this website, and I’m finally writing it up. The short version:

1. In August 2024 I wrote a blog post that documented how a local “independent journalist” had written for white nationalist websites.
2. In October 2024 he filed a DMCA complaint with my host (Netlify).
   1. Netlify support rubber-stamped the complaint without giving me a reasonable way to appeal.
   2. I moved to CloudFlare and cut the blog post back to a few essential facts+links, to make it easier for the next overworked support person to interpret.
3. In February 2025 CloudFlare approved another DMCA complaint from someone who’d copied my entire post to a content mill and backdated it!

This post will mostly focus on the 2nd DMCA complaint, as it’s the most interesting one.

My post was copied to… MormonFind.com?

On February 14, while on vacation, I received the following email:

Cloudflare received the below copyright infringement complaint regarding your account. If the content identified in the complaint is not removed within 48 hours, Cloudflare will take steps to disable access to the content, consistent with section 512(c) of the Digital Millennium Copyright Act. Please note that these steps will include disabling access to the reported URL on which the content is located, which will affect any other content located on the same URL.

Complaint Information:

Reporter’s Name: Aaron Bennet

Reporter’s Email Address: <redacted>

Reporter’s Title: Copyright Infringement

Reporter’s Company Name: Bennet Media Association

Reporter’s Address: <redacted>

Reported URL(s): hxxps://www[.]reillywood[.]com/blog/riley-donovan/

Original Work Description: https://mormonfind.com/2024/04/10/riley-donovan-contributes-to-white-supremacist-websites/

To respond to this issue, please reply to [email protected].

Say you’re working on an agent (a model using tools in a loop). Furthermore, let’s say your agent uses the Model Context Protocol to populate its set of tools dynamically. This results in an interesting UX question: how should you show text tool results to the user of your agent?

You could just show the raw text, but that’s a little unsatisfying when tool results are often JSON, XML, or some other structured data. You could parse the structured data, but that’s tricky too; the set of tools your agent has access to may change, and the tool results you get today could be structured differently tomorrow.

I like another option: pass the tool results to another agent.

The Visualization Agent

Let’s add another agent to our system; we’ll call it the visualization agent. After the main agent executes a tool, it will pass the results to the visualization agent and say “hey, can you visualize this for the user?”

The visualization agent has access to specialized tools like “show table”, “show chart”, “show formatted code”, etc. It handles the work of translating tool results in arbitrary formats into the structures that are useful for opinionated visualization.

And if it can’t figure out a good way to visualize something, well, we can always fall back to text.

Why do it this way?

The big thing is that we can display arbitrary data to the user in a nice way, without assuming much about the tools our agent will have access to. We could also give the main agent visualization tools (tempting! so simple!), but:

1. That can be very wasteful of the context window
   1. Imagine receiving 10,000 tokens from a tool, then the agent decides to pass those 10,000 tokens by calling a visualization tool - the 10,000 tokens just doubled to 20,000 in our chat history
2. The more tools an agent has access to, the more likely it is to get confused
3. A specialized visualization agent can use a faster+cheaper model than our main agent

It’s not all sunshine and roses; calling the visualization agent can be slow, and it adds some complexity. But I like this approach compared to the others I’ve seen, and we’re not far away from fast local models being widely available. If you’ve got another approach, I’d love to hear from you!

This is a brief post about something that confused me a great deal when I started working with LLMs.

Context

Many LLM providers (Anthropic, OpenAI, Google) support “function calling”, AKA “tool use”. In a nutshell:

1. When calling the provider’s chat completion APIs, you tell the model “if needed, I can run these specific functions for you.”
2. The model responds saying “hey go run function X with arguments Y and Z.”
3. You go and run the function with those arguments. Maybe you append the result to the chat so the model has access to it.

Weather lookup is a common example. You tell the model “I have a function get_temperature(city: String) that looks up the current temperature in a city”, and then when a question like “What’s the weather like in Tokyo?” comes up the model responds to your code with “please call get_temperature("Tokyo")”.

Structured Output

All well and good, but where this gets interesting is that function calling is also a good way to get structured data out of LLMs. You can provide a function definition that you have no intention of “calling”, purely to get data in the format you want.

For example, using the Rust genai library:

// Text to analyze
let text = "The quick brown fox jumps over the lazy dog.";

// Define a tool/function for rating grammar
let grammar_tool = Tool::new("rate_grammar")
    .with_description("Rate the grammatical correctness of English text")
    .with_schema(json!({
        "type": "object",
        "properties": {
          "rating": {
            "type": "integer",
            "minimum": 1,
            "maximum": 10,
            "description": "Grammar rating from 1 to 10, where 10 is perfect grammar"
          },
          "explanation": {
            "type": "string",
            "description": "Brief explanation for the rating"
          }
        },
        "required": ["rating", "explanation"]
    }));

// Create a chat request with the text and the grammar tool
let chat_req = ChatRequest::new(vec![
    ChatMessage::system("You are a professional English grammar expert. Analyze the grammar of the given text and provide a rating."),
    ChatMessage::user(format!("Please rate the grammar of this text: '{}'", text))
]).append_tool(grammar_tool);

// ...and execute it
let chat_res = client.exec_chat("gpt-4o-mini", chat_req, None).await?;

The result will include some JSON like:

{
    "rating": 10,
    "explanation": "This sentence is grammatically perfect..."
}

…and we’re done. We just used function calling to get structured data, with no intention of calling any functions. This is much nicer and more reliable than string parsing on the raw chat output.

This approach is probably obvious to many people, but it was unintuitive to me at first; I think “function calling” is a misleading name for this functionality that can be used for so much more.

Alternative Approaches

This isn’t the only way to get structured data out of an LLM; OpenAI supports Structured Outputs, and Gemini lets you specify a response schema. But for Anthropic, it seems like function calling is still recommended:

Tools do not necessarily need to be client-side functions — you can use tools anytime you want the model to return JSON output that follows a provided schema.