DevLens

Name: DevLens
Author: devlensio

by devlensioversion 0.7.2i★ –iRuns on your computer

Registry nameio.github.devlensio/devlens

Install

Overview

Description from the maker (original English)

Query a precomputed code graph (nodes, edges, summaries) of TS/JS/React/Next.js/Node repos via MCP.

PopNx MCP’s three pillars

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

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
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": {
    "devlens": {
      "command": "npx",
      "args": [
        "-y",
        "@devlensio/cli",
        "mcp"
      ]
    }
  }
}

Run this in your terminal

claude mcp add devlens -- npx -y @devlensio/cli mcp

Open in Cursor

Or copy the settings yourself

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

{
  "mcpServers": {
    "devlens": {
      "command": "npx",
      "args": [
        "-y",
        "@devlensio/cli",
        "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": {
    "devlens": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@devlensio/cli",
        "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 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account devlensio, 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 1388 servers in this category
Published to the registry more recently than 82% of the other 1388 servers in this category
Basic checks equal to or better than 100% of the other 1388 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 DevLens 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 (0.7.2) reached the registry 9 days 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

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

GitHub

by github

GitHub’s official server lets AI read and manage code, issues and pull requests in your account. Use GitHub’s hosted version or run it locally with Docker.

Computer or cloud★ 34,245MITUpdated 3 days ago

Install

Desktop Commander

by wonderwhy-er

MCP server for terminal commands, file operations, and process management

Runs on your computer★ 10,225MITUpdated 15 days ago

Install

Cloudflare MCP

by cloudflare

Cloudflare MCP servers

Runs in the cloud★ 4,446Apache-2.0Updated 14 months ago

Install

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.

DevLens

Codebase Intelligence for AI agents and Developers.

Analyze your repo once. Your AI agent then queries a precomputed code graph through MCP — ranked files, call flow, impact, and security — instead of re-reading your codebase file by file. Five self-describing MCP tools, plain-text packets measured at a third of the tokens of reading the code, and the smallest response in its benchmark field.

[Join the DevLens Cloud Waitlist →](https://devlens.io)

Click the image to watch the demo

Why DevLens?

Your agent stops burning tokens. One MCP call returns a task-shaped packet — the files and symbols that matter, ranked, within a token budget. No grep loops, no re-reading files.
It wins the benchmark. First of nine tools on correctness, F1, recall, and precision — the only tool that leads every quality column.
It is fast and small. 67 ms per call, packets 6.7× smaller than the closest graph competitor, and it never exceeds the token budget you set.
Developers get it too. Interactive graph visualization, blast radius before you change a symbol, PR review packets, and per-node security analysis.

Benchmarks

We benchmarked DevLens MCP against the other code graph and retrieval tools — Graphify, codegraph, semble, bm25 — on one headless coding agent, one model, one real TypeScript repository, and 133 real questions written from actual pull requests and symbols:

	DevLens V3 (5 tools)	Graphify	codegraph	semble	bm25
Correctness (agent loop, same run)	0.643	0.619	0.667	0.714	0.667
F1 (agent loop, same run)	0.529	0.526	0.604	0.636	0.581
Correctness (retrieval layer)	0.694	0.662	0.674	0.652	0.664
Response packet	2,230 tokens	15,020 tokens	6,339 tokens	736 tokens	721 tokens
Latency per call	67 ms	2,469 ms	2,736 ms	508 ms	108 ms

Head-to-head against Graphify — the most direct competitor (tree-sitter graph, MCP server) — DevLens wins on every axis: correctness in the agent loop (0.643 vs 0.619, same run) and in the retrieval layer (0.694 vs 0.662, paired 15 wins to 9 losses on identical questions), a 6.7× smaller packet (2,230 vs 15,020 tokens, smaller on all 54 of 54 comparable questions), and ~37× faster per call (67 ms vs 2,469 ms). Graphify's graph is also 64% larger with 3.6× the edges (16,999 nodes / 71,694 edges vs 10,346 / 19,847) for the lower quality.

The pattern behind the numbers: syntactic tools return big blobs or bare line lists. DevLens returns a task-shaped packet from a type-resolved graph — the files that matter, ranked, within your token budget, in milliseconds.

Full methodology, per-type tables, and the nine-tool round: docs/PUBLIC-BENCHMARKS.md

How DevLens compares

DevLens is the only tool in this space that combines native semantic parsing, per-node AI summaries with per-node security analysis, and framework-aware data edges — and the only option you can use commercially under AGPL.

Dimension	DevLens	Graphify	GitNexus	Sourcegraph	DeepWiki
Parsing depth	Native semantic parsers (TS compiler, Python `ast`, `go/types`, JavaParser, `syn`), type-resolved	tree-sitter (syntactic, no type info)	tree-sitter + native bindings (no type info)	SCIP/LSIF symbol index + language servers (no semantic parse)	LLM reads source directly (no structured parser)
Edge quality	Type-checked `IMPLEMENTS`/`EXTENDS`, framework routes (Next.js, Django, Spring, Gin, axum), ORM data edges (`READS_FROM`/`WRITES_TO`)	`EXTRACTED`/`INFERRED`/`AMBIGUOUS` tags, no type or framework awareness	call chains, clusters, `route_map`, no ORM or data edges	precise symbol cross-references (SCIP), no type-checked inheritance	docs-level relationships (no structured graph)
Per-node AI summaries	Technical + business + security with severity on every node	No (LLM used for docs and concepts)	No (embeddings for semantic query)	Via Cody (hover and inline docs, chat-level)	Auto-generated docs per symbol (no security, no technical/business split)
Security analysis	Per-node severity + blast-radius reach with real exploit descriptions	No	Partial (opt-in PDG/taint)	No (compliance certifications only)	No
Agent / MCP integration	CLI + self-describing MCP server + Web UI	CLI + local skill (no MCP)	CLI + 17-tool MCP + hooks (`AGENTS.md`)	MCP server (cross-repo search, not per-repo graph queries)	Unknown (no public MCP integration)
Language coverage	TS/JS, Python, Java, Go, Rust with native parsers for each	12 code families + docs/images (shallow syntactic)	Many via tree-sitter (Dart/Kotlin/Swift), shallow syntactic	30+ via language servers (symbol-level, no semantic edges)	Any (LLM reads source, no structured extraction)
License / pricing	AGPL-3.0, free, including commercial use	Apache-2.0	PolyForm Noncommercial (cannot use commercially)	Open-source core, Enterprise paid	Free for public repos, enterprise tiers unlisted

*(Feature comparison from public sources, Aug 2026.)*

Why teams choose DevLens: semantic edges that syntactic tools cannot produce, per-node security analysis no other open-source tool provides, benchmark-winning retrieval quality, and a single MCP front door — free for commercial use.

Quick Start

Step 1 — Install the CLI

npm install -g @devlensio/cli

Or install the standalone binary (no Node.js needed):

# Linux / macOS

curl -fsSL https://raw.githubusercontent.com/devlensio/devlensOSS/main/scripts/install.sh | sh

Windows (PowerShell)

irm https://raw.githubusercontent.com/devlensio/devlensOSS/main/scripts/install.ps1 | iex

Step 2 — Configure a model (optional)

Only needed if you want AI summaries. Structure-only analysis works offline with no API key.

devlens init

This walks you through picking a provider, API type, key, endpoint, and model interactively — press ESC at any step to go back a stage. See Configuration for recommended models.

Step 3 — Analyze your repo

cd your-project

devlens analyze . --summarize

This builds the graph, generates summaries, and creates the search index — once. After this, everything below is instant.

Step 4 — Connect your AI agent

Start the MCP server:

devlens mcp

Then register it in your agent. Pick your tool:

Claude Code / Claude Desktop

claude mcp add devlens -- devlens mcp

Cursor — add to .cursor/mcp.json:

"mcpServers": { "devlens": { "command": "devlens", "args": ["mcp"] } } }

Codex — add to ~/.codex/config.toml:

[mcp_servers.devlens]

command = "devlens" args = ["mcp"]

Hermes — add to config.yaml:

mcp:

servers: devlens: command: devlens args: [mcp]

Any other MCP client — the server speaks standard MCP over stdio. Point your client at the command devlens mcp. For an HTTP transport instead:

devlens mcp http -p 7000

The server self-describes its full tool list through tools/list, so your agent discovers everything on its own. That's it — ask your agent "how does auth work in this repo?" and watch it query the graph instead of reading files.

What your agent can ask

The server self-describes every tool's parameters through tools/list — your agent fills them in automatically. Five tools, no `graphId` anywhere — the repo is resolved from your working folder.

Tool	Key arguments	What it does
`resolve_context`	`task` (required), `intent?`, `focus?`, `tokenBudget?`	The front door. One call returns a task-shaped plain-text packet: an ANSWER line, ranked nodes with one-line meanings, call flow, involved files with matched-line snippets, and an id map — all within a token budget. Intents: `pinpoint`, `reference-list`, `flow`, `overview`, `concept`, `security-audit`, `exploratory`.
`find_symbols`	`query` (required), `limit?`	Cheap BM25F name lookup: nodeIds plus `file:line`, no graph, no source.
`get_node`	`node` (required), `include?`	Full detail for one node: technical, business, and security summaries plus callers, callees, and (opt-in) raw source.
`impact`	`target?` or `from`+`to` commits	Change impact: what breaks if a symbol changes; commit-range diff mode for two analyzed commits.
`repo`	`action`	Manage analyzed graphs: `list`, `analyze`, `freshness`, `health`.

Every response carries a provenance block (commitHash, analyzedAt, hasGit), so your agent always knows which snapshot answered the question.

Full reference: docs/mcp-reference.md

Supported languages

DevLens parses five language families with native parsers (no AI, no regex, no tree-sitter) and understands their frameworks. Summaries are never generated silently: you confirm before tokens are spent, and structure-only analysis needs no provider at all.

Language	Frameworks / stacks the graph understands	What gets parsed
TypeScript / JavaScript	React, Next.js (app and pages router), Express/Hono/Fastify, React Router, TanStack Router, any Node	components, hooks, state stores, classes, methods, functions, routes
Python	FastAPI, Flask, Django (+DRF), SQLAlchemy / Django ORM, Celery, Pydantic	classes, methods, functions, routes, data models
Java	Spring Boot (controllers, JPA, Spring Data repositories)	classes, methods, interfaces, enums, routes
Go	net/http, Gin, Echo, chi, Fiber, GORM, database/sql	structs, interfaces, methods, functions, routes
Rust	axum, actix-web, rocket, utoipa, Diesel	structs, enums, traits, impl blocks, methods, functions, routes

Each repo is analyzed with its language's own parser (Python ast, JavaParser, Go go/ast plus go/types, Rust syn, TS compiler API), so edges are real: type-checked interfaces (IMPLEMENTS), framework routes (HANDLES), and ORM data layers (READS_FROM, WRITES_TO).

For developers: the Web UI

*For when you want to see your codebase laid out as an interactive graph.*

Open the Web UI, paste your repo path, and explore a force-directed canvas. Click any node to see its summaries, callers, callees, and security flags. Search, filter, and diff commits across versions. The Project panel shows search index readiness with a one-click reindex action.

bun start   # from the source repo: API + Web UI on one port (http://localhost:3000)

The Web UI runs from the source tree, it is not bundled into the installed CLI binary.

CLI

*For scripts, CI, and fast answers without leaving the terminal. Every command supports --json for piping into scripts, -v/--verbose for diagnostics, and --quiet for minimal output.*

Analyze and summarize

Command	What it does
`devlens detect [path] [--deps]`	Inspect a repo before analyzing: language, manifest, dependency count, source files

| devlens analyze [path] [--summarize] [--force-summarize] | Build the graph and its search index (optionally add AI sum

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

DevLens

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

DevLens

Why DevLens?

Benchmarks

How DevLens compares

Quick Start

Step 1 — Install the CLI

Windows (PowerShell)

Step 2 — Configure a model (optional)

Step 3 — Analyze your repo

Step 4 — Connect your AI agent

What your agent can ask

Supported languages

For developers: the Web UI

CLI