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Type Atlas

by tyler-mitchellversion 1.0.0i★ –iRuns on your computer
Registry nameio.github.tyler-mitchell/type-atlas

Install

Overview

Description from the maker (original English)

Editor-grade TypeScript, Markdown, and JSON intelligence for coding agents.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • npmNode.js installed (for the npx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "type-atlas": {
      "command": "npx",
      "args": [
        "-y",
        "@type-atlas/mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add type-atlas -- npx -y @type-atlas/mcp

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "type-atlas": {
      "command": "npx",
      "args": [
        "-y",
        "@type-atlas/mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "type-atlas": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@type-atlas/mcp"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account tyler-mitchell, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

  • More stars than 0% of the other 32678 servers in this category
  • Published to the registry more recently than 31% of the other 32678 servers in this category
  • Basic checks equal to or better than 100% of the other 32678 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Type Atlas safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.0.0) reached the registry 2 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Starreel

Alternatives

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Runs on your computer★ 440MITUpdated 14 months ago
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Remote Desktop Commander

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Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

Runs in the cloud★ 211Updated 2 days ago
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Starreel

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Turn a script into a finished, downloadable short-drama episode — via MCP or REST.

Runs on your computerUpdated today
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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Type Atlas is what I have all my code agents use across all of my TypeScript projects for all code navigation needs. Most of those projects are monorepos. Some are large and complex enough that understanding how a change fits into the rest of the system is a real part of the work. This tool was designed as a complete replacement for an agent's default code navigation methodology.

I've been iterating on this tool for months based on how my own coding agents actually work in my projects. Most of what Type Atlas does, exists because I kept seeing the same problems:

  • Agents reason from an incomplete view of the system. They may understand the file they found while missing the surrounding code that determines how it is supposed to be used.
  • Agents rebuild things that already exist. The capability is already in the repo, but the agent never finds it because it does not know what it is called.
  • Agents often read either too little code (Claude) or far too much (Codex). Too little leaves them making decisions without enough surrounding context. Too much fills the context window with entire files and implementation detail that never mattered to the task.
  • Agents repeatedly stop to run type checks just to find errors an IDE would already be showing. That adds latency throughout implementation and pushes feedback later than it needs to arrive.
  • Agents are fundamentally operating blind. String-search navigation never gives them a strong internal atlas of the codebase. They are forced to make implementation decisions from fragments of files and matches they happened to retrieve, with important structure and compiler-known information still missing.

As projects grow, these problems compound and start showing up directly in the quality of the code agents write.

Semantic code navigation

Coding agents navigate code through file reads and string searches by default. That gives them source text and leaves the model to reconstruct relationships that the TypeScript language service already knows.

Type Atlas gives the agent direct access to that semantic information:

  • A symbol resolves to its actual definition and references rather than every occurrence of the same text.
  • Callers and implementations are identified by their relationship to the symbol.
  • Inferred types come from the language service instead of being reconstructed from nearby source.
  • Results keep their source range and owning TypeScript project attached.

A text match is still useful when text is what the agent is looking for. It is a weak substitute for semantic navigation when the question is about the program itself.

Find what already exists

Large codebases contain useful code that an agent has no reason to know by name. String search works best after the agent already knows enough vocabulary to formulate the search.

Type Atlas gives it other ways in:

  • The agent can describe behavior in natural language and find code based on what it does.
  • A discovered result leads back to a real symbol and an exact source range.
  • From that symbol the agent can follow actual relationships through the codebase instead of guessing another identifier to search for.

This is especially useful in large monorepos. Existing helpers and established implementations are easier to discover before the agent decides it needs to create another one.

Diagnostics during normal work

A developer in an IDE sees compiler feedback while working. Coding agents usually get that feedback by stopping implementation to run a type-check command and then waiting for the result.

Type Atlas moves much of that feedback into work the agent was already doing:

  • Relevant diagnostics can arrive with normal code-intelligence responses.
  • Errors show up while the affected code is still part of the agent's current working context.
  • A bad type assumption can be caught before it turns into several dependent edits.

I still use full type checks for verification. They do not need to be the main way an agent learns about errors while it works.

Responses are built for model context

Making a response smaller is useful only when the information removed was unnecessary. Models also use the organization of what remains.

Type Atlas treats structure as part of the information:

  • Labels make the role of a result explicit.
  • Grouping keeps related facts together.
  • File boundaries stop unrelated source from blending together.
  • Source locations stay attached to the thing they describe.
  • Trees keep their hierarchy instead of becoming a flat sequence.
  • Diagnostics retain the source needed to understand them.

The same principle determines what gets omitted. Function bodies can stay folded when the signature is enough. Repeated serialization and unrelated source do not need to occupy the context window simply because they were available.

The goal is useful information density. Fewer tokens matter, but removing the structure that helps the model understand those tokens would defeat the point.

Information for the next decision

Every tool call is part of the agent's reasoning process. A good response should answer the current question while leaving the agent in a better position to decide what to inspect next.

Type Atlas keeps useful follow-up information close to the result that exposed it:

  • A symbol can arrive with the relationships needed to understand how it participates in the codebase.
  • Repository structure can carry line counts and working-tree state alongside the files themselves.
  • Search results include concrete source ranges that can be followed directly.
  • Project context remains attached as the agent moves from one result to another.

This gives the agent better evidence at each branch in its investigation. It can follow relationships that actually exist in the program instead of treating every textual match as an equally meaningful lead.

The benefit is higher-quality navigation. Each step preserves more of the information needed to choose the next one.

Project and scope awareness

TypeScript questions depend on project context. That becomes especially important in a monorepo, where an answer can be correct inside one project while still being incomplete for the repository as a whole.

Type Atlas keeps those boundaries visible:

  • Files are resolved through the TypeScript project that owns them.
  • Results state the project scope they came from when that scope matters.
  • Counts make the size of a result explicit.
  • Limits are stated when an answer covers less than the whole repository.
  • Source locations can be passed directly into later calls.

The agent gets enough information to understand what an answer actually covers before relying on it.

Built from daily agent use

I use Type Atlas with my own coding agents every day across all of my TypeScript projects. The current behavior came from repeated use.

A lot of the design can be traced directly to recurring agent behavior:

  • Reads fold because agents were spending context on bodies they did not need.
  • Diagnostics travel with normal responses because repeated type-check commands were wasting implementation time.
  • Semantic relationships are grouped because agents kept reconstructing the same information through separate calls.
  • Natural-language code search exists because useful code often has a name the agent could never infer from the task.

That is still how I work on Type Atlas. When I keep seeing agents waste time on the same navigation problem or repeatedly miss the same kind of information, I change the tool.

The examples below are captured from the running server against a fixture monorepo and regression-checked with the implementation.

Install

codex mcp add type-atlas -- npx --yes @type-atlas/mcp@latest

claude mcp add --scope user type-atlas -- npx --yes @type-atlas/mcp@latest

code --add-mcp '{"name":"type-atlas","command":"npx","args":["--yes","@type-atlas/mcp@latest"]}'

Any other client takes the standard shape:

{

"mcpServers": { "type-atlas": { "command": "npx", "args": ["--yes", "@type-atlas/mcp@latest"] } } }

A client that starts servers without your shell PATH will not find npx by name; give the absolute path from which npx when that happens. On Windows, a client that cannot launch the npx.cmd shim needs "command": "cmd" with "args": ["/c", "npx", "--yes", "@type-atlas/mcp@latest"].

Clients read MCP config at startup, so restart after. @latest resolves on every process start; pin a version if you do not want tool behavior moving under you.

search_code, related_code, investigate_code, and search_dependency_code run a semantic index through uvx and need uv. Without it those four report that uv is missing, explore_symbol drops its related-code section, and the rest is unaffected.

Recommended

Installing the server does not change what an agent reaches for. Some agents, Claude among them, will assemble whatever their shell allows, chained together, and produce a fresh justification each time, so naming a few commands to avoid does not hold. The instruction has to rule out the whole category and name the exceptions. Add this to AGENTS.md or CLAUDE.md:

Type Atlas MCP is the required tool for reading and navigating code in

TypeScript and JavaScript. This is not a preference. No shell command is an

acceptable substitute, whatever it is composed of, and neither is a plain

file read. The only valid fallbacks are a server that is down, a call that

errored, or a file that is neither TS nor JS.

--require-intent

This opt-in flag requires one decision sentence for broad exploration tools such as repository search and workspace symbols. Targeted reads and semantic lookups stay unaffected, and intent is never echoed into tool responses.

Tool call results

Paths are workspace-relative, coordinates are one-based, so a location in one answer is valid input to the next call. Editing tools return patches; nothing is written for you.

Everything below is captured from the running server against fixtures/ledger by the scenario suite, which replays the same calls and fails on drift. Nothing here is hand-written, and changing what a tool answers changes this file in the same commit. The source is README.mdoc. Every tool has a page with more cases in docs/tools.

list_files

Structure, line counts, and git status in one tree, using the badge letters editors already use. Deleted files get a row even though they exist only in git's answer. Folded directories say what they hold rather than disappearing.

Agent's Input

tool: List files

workspace: fixtures/ledger

working tree arranged: currency.ts edited · rounding.ts created · index.ts deleted

directory: packages/money depth: 2

answered in 57ms

Response

packages/money/

├ src/ · 3 changed │ ├ currency.ts · 21 loc · M +2 │ ├ index.ts · D -12 │ ├ money.ts · 58 loc │ ├ rounding-mode.ts · 15 loc │ └ rounding.ts · 11 loc · U ├ tests/ │ ├ money.test.ts · 15 loc │ └ rounding-parity.ts · 15 loc ├ package.json · 19 loc └ ts

Only the beginning is shown; read the rest in the repo

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