Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Description from the maker (original English)
Authenticated local evidence paths for JavaScript and TypeScript coding agents.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
You need
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Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"madar": {
"command": "npx",
"args": [
"-y",
"@lubab/madar",
"mcp"
]
}
}
}Run this in your terminal
claude mcp add madar -- npx -y @lubab/madar mcpPaste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"madar": {
"command": "npx",
"args": [
"-y",
"@lubab/madar",
"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.
Paste into .vscode/mcp.json in your project
{
"servers": {
"madar": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@lubab/madar",
"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.
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These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
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
The registry name was verified with the GitHub account mohanagy, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
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.
The registry does not list a required key, though some servers ask you to sign in on first use.
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).
The GitHub repo last changed –, and the latest version (0.40.0-beta.1) reached the registry 3 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: Spec Driven Development, Remote Desktop Commander, HotCodePush
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
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Manage over-the-air (OTA) updates for React Native, Expo, Capacitor and Cordova apps.
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Retrieve specific source code for your coding agent.
Madar builds a local repository graph for JavaScript and TypeScript function lookup by file and symbol. Agents can inspect returned source and request omitted lines.
Broad context discovery and workflow packs are experimental: they can omit necessary evidence and increase total tokens once follow-up reads are counted. Consistent whole-task accuracy and token savings have not been established. Use direct source reads when they are sufficient.
Install Madar with Node.js 20 or newer. For an experimental discovery preview, run it inside your repository:
npm install -g @lubab/madar
cd your-repository madar try "how does authentication work?"
madar try builds or reuses the local graph, prints an experimental source preview, and shows an optional agent-install command. It does not modify your source code.
For a concrete example, Madar's included password-reset workspace contains this path:
account-routes.ts
-> PasswordResetService.requestPasswordReset() -> userRepository.saveResetToken() -> enqueueResetEmailJob() -> sendPasswordResetEmail()
This is an example of a partial starting path. The agent must verify the conditions, dependencies, and failure paths needed for its answer.
Choose the agent you use. For Claude Code:
madar claude install --profile strict
madar doctor madar status
The compact strict profile exposes context_pack and context_expand; full also includes them, while core exposes graph-navigation tools. After installing, run madar doctor and madar status.
When the file and function are known, use this MCP request:
{
"prompt": "Read these functions", "task": "explain", "targets": [{"file": "lib/util.js", "symbol": "join"}], "budget": 1500 }
Replace the example file and symbol with those in your repository. Targets select function source, not a complete workflow; inspect omissions and returned continuation handles. A direct source read remains appropriate.
Without exact targets, questions such as these use experimental discovery:
How does authentication work?
Why does this endpoint return 403? Where is the report generated? What breaks if I change this service? Add telemetry to this flow.
For MCP source lookup and recovery, use strict. Codex and OpenCode install it automatically; Aider installs prompt guidance without an MCP server.
| Agent | Install command |
|---|---|
| Claude Code | madar claude install --profile strict |
| Codex CLI | madar codex install |
| Cursor | madar cursor install --profile strict |
| GitHub Copilot | madar copilot install --profile strict |
| Gemini CLI | madar gemini install --profile strict |
| Aider | madar aider install |
| OpenCode | madar opencode install |
Installer details are in the CLI and MCP reference. Step-by-step setup and smoke tests are in the agent quickstarts.
After upgrading Madar, rerun your agent's install command to refresh its managed profile. Older profiles may lack automatic refresh or Codex's longer startup window.
During automatic reconciliation, graph-backed calls return a retryable response. If continuing with Madar, retry after its suggested delay. Native source investigation remains available.
context_pack returns source locations, excerpts, omissions, freshness, and advisory coverage. Its compact source_evidence_v2 response has a serialized token budget, including metadata. A small packet does not establish a cheaper or more accurate investigation.
For exact lookup, use up to eight JavaScript or TypeScript targets, each with a workspace-relative file and case-sensitive symbol. Only explain mode is supported. Missing, ambiguous, excluded, or stale owners are refused. Targets select functions, not complete workflows; they do not infer calls.
To inspect a known call instead, add "callee":"scan" to its caller target. Madar checks the full current caller and returns a uniquely bound local implementation. Identifier and static dotted calls are supported; nested-closure calls and dynamic expressions are excluded. Unverified bindings are refused.
One oversized exact function request returns a budgeted first page; continuations retrieve remaining lines without repetition.
For omitted evidence:
source_expand_handle with context_expand in the same MCP session. Continue with each returned handle until the remaining whole source lines are delivered. Changed or newly excluded source invalidates the handle.expandable handle for related evidence. An optional exact symbol selects a verified function inside that handle's scope. Do not pass a symbol with a source-continuation handle.budget_insufficient, retry with a larger budget or request fewer targets. Minimum budget: 128 tokens; expansion defaults to 1500.A snippet_ref points to shared source text; use the referring node's snippet_line_range for its excerpt. When line_number is omitted, use that range's start. Response paths may be workspace-relative.
Experimental discovery can include source-resolved dependencies and static graph relationships. source_dependencies reports verified calls and unresolved locations; selection_omitted reports exact matches omitted by the lookup limit. Unresolved details may use either a flat list or grouped calls, including repeated line_numbers. Counts can exceed the bounded examples shown. None of these establishes runtime execution or whole-task completeness.
Regenerate older graphs to include newly supported source forms. Root/workspace package.json objects up to 128 KiB and JavaScript/TypeScript files under lib/ are eligible under source policy. Exclude generated output with .madarignore. Other JSON configuration files are not source evidence.
The MCP response shape describes the response contract.
The CLI can generate and inspect context without installing an agent integration:
madar generate .
madar summary madar pack "how does auth work?" --task explain --format text
By default, madar generate . combines SPI metadata with proven legacy semantics for JavaScript/TypeScript, and uses legacy fallback for other supported languages. Strict modes are in the CLI reference.
Legacy updates rebuild the current corpus when files change or disappear, refreshing incoming calls and imports from unchanged files. Parser caches can still be reused; unchanged graphs are reused after any required extractor-version upgrade.
Create a provider-ready prompt:
madar prompt "how does auth work?" --provider claude
Create a share-safe handoff for another coding tool:
madar handoff "add auth telemetry" --task implement --consumer copilot
See the getting-started tutorial for a sample workspace.
Consider precise source lookup when you know the JavaScript or TypeScript file and function and need selected bodies with explicit omissions and continuations.
It helps less when the repository is small, one direct read is sufficient, source is stale, or the question depends on dynamic or live runtime behavior. Architecture, review, and impact discovery remain experimental.
Madar complements agents and IDE indexing. It is not a hosted knowledge base, runtime tracer, PR reviewer, or vulnerability scanner.
.env*, credential stores, and known non-source secret material are excluded. This is a path policy, not a content-level secret scanner.madar generate .; strict workflows can require --require-fresh-context or --require-fresh-graph.Treat every local MCP install, hook, or agent profile as part of your local trust boundary. Read the MCP threat model.
Madar publishes the prompts, answers, traces, and share-safe reports behind its benchmark statements. Two public experiment types answer different questions and should not be compared as if they were the same test.
Six June TypeScript runtime-flow trials used a source checkout with task-specific proof profiles. In those controlled runs, Madar was invoked once per row and the recorded results showed:
3.5x to 18.5x fewer tool calls2.2x to 15.6x less provider-reported input1.65x to 7.09x lower latencyThose receipts are real measurements of profile-assisted Madar. They demonstrate what the workflow can achieve when the correct task evidence is available. They are not evidence that an untuned npm installation will reproduce the same result for arbitrary questions, because the old prompts and checkout retrieval contained benchmark-specific obligations unavailable to normal package users.
The July reruns removed that assistance and used the same isolated, unpacked @lubab/madar@0.31.0 package artifact. Four of six repositories recorded an agent-adoption failure: no attributable Madar MCP call occurred. The other two invoked Madar but failed strict prompt or answer gates. The correct result is zero valid performance comparisons, not six product losses. These reruns expose adoption and answer-completeness work; they neither confirm nor refute the earlier controlled efficiency measurements.
Read the benchmark suite and all dated receipts or the shorter claims and evidence map.
Current version: 0.33.0.
See the 0.33.0 changelog.
| Need | Start here |
|---|
| First run | [Getting started](https://github.com/mohanagy/madar/blob/main/docs/tutorial
Only the beginning is shown; read the rest in the repo