Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Beschreibung vom Entwickler (englisches Original)
Authenticated local evidence paths for JavaScript and TypeScript coding agents.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"madar": {
"command": "npx",
"args": [
"-y",
"@lubab/madar",
"mcp"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add madar -- npx -y @lubab/madar mcpIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"madar": {
"command": "npx",
"args": [
"-y",
"@lubab/madar",
"mcp"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"madar": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@lubab/madar",
"mcp"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 6. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto mohanagy bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.40.0-beta.1) kam vor 3 Monaten in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Node.js auf deinem Computer.
In derselben Kategorie: Spec Driven Development, Remote Desktop Commander, Apify MCP Server
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
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.
Extract data from any website with thousands of scrapers, crawlers, and automations on Apify Store ⚡
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
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
Nur der Anfang wird gezeigt; den Rest findest du im Repo