Contextatlas

Name: Contextatlas
Author: traviswye

by traviswyeversion 1.0.1i★ –iRuns on your computer

Registry nameio.github.traviswye/contextatlas

Install

Overview

Description from the maker (original English)

Pre-computed atlas of your codebase for Claude Code: LSP + ADRs + git. 45-72% token reduction.

PopNx MCP’s three pillars

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User voiceiNo reviews yetReviews open together with accounts in a later step
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UsageiNot measured yet★ –

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: Developer tools

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?

ClaudeClaude CodeCursorVS CodeChatGPT

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": {
    "contextatlas": {
      "command": "npx",
      "args": [
        "-y",
        "contextatlas"
      ]
    }
  }
}

Run this in your terminal

claude mcp add contextatlas -- npx -y contextatlas

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "contextatlas": {
      "command": "npx",
      "args": [
        "-y",
        "contextatlas"
      ]
    }
  }
}

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": {
    "contextatlas": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "contextatlas"
      ]
    }
  }
}

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.

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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 traviswye, 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 Developer toolsi

More stars than 0% of the other 1394 servers in this category
Published to the registry more recently than 22% of the other 1394 servers in this category
Basic checks equal to or better than 100% of the other 1394 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 Contextatlas 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 (1.0.1) reached the registry 6 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: GitHub, Desktop Commander, Cloudflare MCP

Alternatives

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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.

ContextAtlas

Stop watching Claude burn tokens grepping for context it can't possibly find.

ContextAtlas turns your codebase into a *single-call* context bundle for Claude Code — fusing LSP-grade structure, architectural intent from your Architectural Decision Records (ADRs), git history, and test associations. Measured 45-72% token reduction with zero quality regression across benchmark axes on architectural prompts across the hono / httpx / cobra benchmark suite.

Quick start → · Benchmark results → · Why not graph-based? → · Architecture → · ADRs →

ContextAtlas ships two equivalent paths — CLI and Claude Code Skills — both producing the same atlas.json. See Quick Start for setup.

The Problem

Claude Code currently learns your codebase by brute force. Every session starts fresh. Every "where is X?" triggers multiple grep calls. Every "what depends on Y?" is another flurry of file reads. On a mid-sized codebase, answering a single architectural question can consume 40+ tool calls and 100,000+ tokens before Claude has enough context to reason well.

Worse: the architectural intent that governs your code — the ADRs, the design decisions, the "we did it this way because" — is invisible to Claude. The rule that OrderProcessor must be idempotent lives in docs/adr/. When Claude proposes a change, it has no way to know that constraint exists.

Yesterday's understanding doesn't carry to today. Every conversation starts from zero. Your ADRs, your commit history, your test coverage — none of it is on the agent's table.

What if expensive understanding happened once, at index time, and every query became a dictionary lookup?

That's ContextAtlas.

What ContextAtlas Is

ContextAtlas is an MCP server that gives Claude Code a curated atlas of your codebase — fusing LSP-grade structural precision with architectural intent extracted from your ADRs, docs, and git history, delivered to Claude in single-call context bundles.

Every bundle Claude receives combines four independent signals about a symbol:

Structural data from the language server — definition, references, types, diagnostics. Compiler-grade precision.
Architectural intent from your ADRs, READMEs, and design docs — structured claims extracted by Opus 4.7 at index time, keyed to specific code symbols.
Historical context from git — recent commits touching the symbol, hot/cold indicators, co-change patterns.
Test associations — which tests reference the symbol, where coverage lives.

One MCP call returns all four, fused. No ADRs in your repo yet? You still get LSP + git + tests in one call instead of fifteen — a meaningful baseline improvement. Add ADRs and the bundles get richer. The architecture is designed so any subset of signals produces value.

Given an ADR stating that OrderProcessor must be idempotent, a call to get_symbol_context("OrderProcessor") returns:

SYM OrderProcessor@src/orders/processor.ts:42 class

SIG class OrderProcessor extends BaseProcessor INTENT ADR-07 hard "must be idempotent" RATIONALE "All order processing must be safely retryable." INTENT ADR-12 soft "prefer async base class for new processors" REFS 23 [billing:14 admin:9] TOP ref:ts:src/billing/charges.ts:88 TOP ref:ts:src/admin/orders.ts:12 GIT hot last=2026-03-14 RECENT "Fix idempotency bug in retry path" a3f2c1d TESTS src/orders/processor.test.ts (+11)

When Claude is asked to modify OrderProcessor, it sees the idempotency constraint *before* proposing changes — not after a user review catches the violation.

Who this is for. ContextAtlas is built for the average developer using Claude Code on real codebases — not just engineers at large orgs working on 500,000-file monorepos. Token-burn reduction scales with codebase size — dramatic on a 200-file framework, modest on a 30-file library. But architectural intent capture is size-invariant. A 30-file library can have meaningful architectural decisions worth surfacing, and Claude respecting them matters just as much as on a larger codebase.

Beyond tokens: a design-alignment case study

Efficiency and quality are necessary but not sufficient. The substantive value of context-grounding shows up in design choices on non-trivial code-change tasks.

A/B trial during v0.3 development. Identical 3-paragraph prompt across two ContextAtlas clones: implement a known bug fix — locate the bug, design and implement the fix, write tests, document via ADR. The only setup difference: MCP availability.

Arm	MCP	Approach selected
A (vanilla)	none	Recall-first approach (broader matching; fought the project's precision-thesis with noise)
B (CA-aided)	ContextAtlas	Precision-optimization approach (aligned with the project's pre-extracted-claims-with-structural-attribution thesis)

Arm B's approach landed in main. Both arms functionally fixed the bug at similar wall-clock and token cost. The substantive difference was *alignment with project design thesis* — the CA-aided arm could read the relevant ADR + prior architectural work from the atlas, and made a choice that fit. The vanilla arm couldn't see that context and chose an approach that worked but fought the architecture.

Arm A's substantive consideration wasn't lost — captured as future-work investigation trigger. The recall-vs-precision tradeoff is preserved.

Full synthesis at v0.3 Round 3 dogfood evidence.

N=1 trial; this is anecdote, not benchmark. The systematic benchmark suite (hono / httpx / cobra) measures efficiency and quality (see §The Numbers below). This A/B trial measures the substantively-distinct *design-alignment* axis — which doesn't fit benchmark-suite methodology (every code-change task is repo- specific) but is the substantive value proposition for cohort developers building on real codebases.

The Numbers

We benchmark ContextAtlas against baseline Claude Code on three repositories chosen to reflect realistic developer workloads:

Repo	Language	Source files	Role
honojs/hono	TypeScript	186	Mid-sized framework
encode/httpx	Python	23	Focused production library
spf13/cobra	Go	19	CLI framework

Methodology. 24 prompts per repo, 6 task buckets, blind manual grading, pre-registered rubric, no cherry-picking. Full methodology in RUBRIC.md.

Efficiency: 50-71% tool-call reduction on architectural prompts

Phase 5 reference run on hono, six pre-registered prompts:

Prompt	Bucket	Alpha calls	CA calls	Δ	Alpha $	CA $
h1-context-runtime	win	18	9	−50%	$2.36	$1.52
h2-router-contract	win	11	5	−55%	$0.60	$0.53
h3-middleware-onion	win	5	5	0%	$0.38	$0.47
h4-validator-typeflow	win	21	6	−71%	$2.95	$0.52
h5-hono-generics	tie	11	13	+18%	$0.79	$1.17
h6-fetch-signature	trick	3	4	+33%	$0.17	$0.29
aggregate		69	42	−39%	$7.25	$4.50 (−38%)

The headline case: h4-validator-typeflow ran 7.3× cheaper ($2.95 → $0.52) at equivalent answer depth. CA opens with the governing ADR by number; the baseline reconstructs the architecture from source. Tie and trick buckets (h5, h6) show CA net-negative as the rubric predicted — CA over-engineers on questions where architectural intent doesn't carry load. Bucket-aware methodology surfaces these expected cases rather than burying them.

Cross-language replication: the same architectural-intent win mechanism holds on Python (Phase 6 — httpx) and Go (Phase 7 — cobra). Phase 8 re-ran the locked prompt sets against v0.3-sharpened atlases at the same pinned target SHAs: 45-72% token reduction on architectural-intent prompts across all three target languages. Full synthesis at phase-8-v0.3-reference-run.md.

Quality: blind-graded, paired-t with confidence intervals

v0.5 shipped the LLM-judge methodology under paired-mode anonymization (per ADR-19). Cross-cell rollup paired-t at N=27 differences per axis (5 anchor cells × n=5 trials × 2 conditions; hono h1 auto-stretch to n=8):

Quality axis	Mean Δ (0-3 scale)	95% CI	Tier
Factual correctness	+0.370	[0.176, 0.565]	CLEAN
Hallucination	+0.296	[0.032, 0.561]	Borderline
Actionability	+0.148	[0.005, 0.291]	Borderline
Completeness	+0.037	[-0.039, 0.113]	Not distinguishable

Threshold pre-registration: the three-tier framing (≥0.05 CLEAN; 0.001-0.05 BORDERLINE; ≤0 NOT distinguishable) was locked before precision values were computed. No goalpost-shifting after data. 76% tie rate confirms anonymization worked — the judge couldn't tell which condition was which on three-quarters of comparisons.

Full per-axis numerics + 9 named findings at the Phase-9 reference doc. Honest methodology limits documented in §Methodology and Honest Limits below.

How ContextAtlas Compares to Alternatives

A few deliberate framings — what ContextAtlas is and isn't relative to neighboring tools:

vs. graph-based code intelligence (Graphify and similar). We're in the same category — both build pre-computed indexes over codebases for LLM agents via MCP. That's genuine category overlap, and we want to be straight about it. Where we differ:

LSP-grounded vs. heuristic-extracted. ContextAtlas delegates all structural questions to the language server (tsserver, Pyright, gopls, ruby-lsp, csharp-ls). Graphify derives structure via parsing and extraction.
Pre-composed bundles vs. graph primitives. ContextAtlas's MCP tools return fused bundles in one call. Graphify exposes graph operations (graph_query, get_neighbors, shortest_path) that callers compose.
Narrow scope vs. broad scope. ContextAtlas indexes code + prose
git. Graphify ingests documentation, diagrams, research papers, and more.
Claim-first vs. graph-first. ContextAtlas stores discrete claims with severity labels, optimized for "what constrains this symbol?" Graphify models the world as nodes and

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

Contextatlas

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Developer toolsi

Common questions

Alternatives

GitHub

Desktop Commander

Cloudflare MCP

README from the maker

ContextAtlas

The Problem

What ContextAtlas Is

Beyond tokens: a design-alignment case study

The Numbers

Efficiency: 50-71% tool-call reduction on architectural prompts

Quality: blind-graded, paired-t with confidence intervals

How ContextAtlas Compares to Alternatives