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PopNx MCPServidores MCP para quienes usan IA · explicados con palabras sencillas

Forensic Deepdive

de Dhevenddraversión 0.10.0i★ –iSe ejecuta en tu computadora
Nombre en el registroio.github.Dhevenddra/forensic-deepdive

Instalar

Resumen

Descripción del creador (original en inglés)

Persistent code knowledge graph + 9 composite MCP tools for AI coding agents.

Los tres pilares de PopNx MCP

Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.

Qué puedes hacer con él

Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.

  • Conexión: Se ejecuta en tu computadora
  • Transporte: stdio
  • Categoría: Búsqueda y memoria de IA

Instalación

Elige la app de IA que usas y copia la configuración.

Antes de instalar

Necesitas

  • PyPIuv de Python instalado (para el comando uvx)

¿Qué app usas?

Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.

Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json

{
  "mcpServers": {
    "forensic-deepdive": {
      "command": "uvx",
      "args": [
        "forensic-deepdive",
        "serve",
        "--repo",
        "."
      ]
    }
  }
}

Ejecuta esto en tu terminal

claude mcp add forensic-deepdive -- uvx forensic-deepdive serve --repo .

Abrir en Cursor

O copia la configuración tú

Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json

{
  "mcpServers": {
    "forensic-deepdive": {
      "command": "uvx",
      "args": [
        "forensic-deepdive",
        "serve",
        "--repo",
        "."
      ]
    }
  }
}

Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.

Abrir en VS Code

O copia la configuración tú

Pégalo en .vscode/mcp.json de tu proyecto

{
  "servers": {
    "forensic-deepdive": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "forensic-deepdive",
        "serve",
        "--repo",
        "."
      ]
    }
  }
}

Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.

Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.

Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.

Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.

Qué puede hacer

Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.

Revisiones básicasi

Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 6 nov 2026

  • ✓ Superada

    El creador coincide con el repo

    El nombre en el registro se verificó con la cuenta de GitHub Dhevenddra, dueña del repo

  • … Aún sin revisar

    Sigue en mantenimiento

    Todavía no hemos leído este repo en GitHub

  • … Aún sin revisar

    Tiene licencia

    Todavía no hemos leído este repo en GitHub

  • – No aplica

    El endpoint responde

    Este se ejecuta en tu computadora; no hay endpoint en internet

  • – No aplica

    Inicio de sesión con OAuth

    Se ejecuta en tu computadora y usa ajustes locales en su lugar

  • … Aún sin revisar

    Sin instrucciones ocultas en las descripciones de herramientas

    Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet

Comparado con otros en Búsqueda y memoria de IAi

  • Más estrellas que el 0% de los otros 7248 servidores de esta categoría
  • Publicado en el registro más recientemente que el 26% de los otros 7248 servidores de esta categoría
  • Revisiones básicas iguales o mejores que el 100% de los otros 7248 servidores de esta categoría

Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.

Preguntas frecuentes

¿Es seguro Forensic Deepdive?

Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.

¿Qué claves o códigos necesita?

El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.

¿Se ejecuta en mi computadora o en la nube?

Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).

¿Sigue en mantenimiento?

El repo de GitHub cambió por última vez –, y la última versión (0.10.0) llegó al registro hace 3 meses.

¿Qué apps pueden usarlo?

Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Python (uv) en tu computadora.

¿Qué puedo usar en su lugar?

En la misma categoría: Context7, Codebase Memory, Exa

Alternativas

Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.

Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi

Context7

de upstash

Up-to-date docs for AI agents

Computadora o nube★ 64.224MITActualizado hoy
Instalar

Codebase Memory

de DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Se ejecuta en tu computadora★ 48.148MITActualizado hace 20 días
Instalar

Exa

de exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Se ejecuta en la nube★ 5213MITActualizado el mes pasado
Instalar

README del creador

Abrir el README

Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.

forensic-deepdive

`forensic-deepdive` gives an AI coding agent forensic understanding of an unfamiliar codebase — a persistent knowledge graph, an MCP server, and five durable markdown artifacts, so your agent stops re-discovering the same architecture from scratch every session.

uv tool install forensic-deepdive && forensic extract /path/to/repo

Why this is different: every edge and every emitted claim carries a confidence tag — EXTRACTED (deterministic fact) / INFERRED (a heuristic resolved cleanly) / AMBIGUOUS (multiple candidates, shown, not guessed) — so your agent knows what to trust *before* it acts on it. Nothing else in this category tags confidence at all.

Why not GitNexus / CodeGraphContext / DeepWiki / Sourcegraph

forensic-deepdiveGitNexusCodeGraphContextDeepWikiSourcegraph
LicenseApache-2.0PolyForm NoncommercialMITproprietary (open variant: MIT)partial
Persistent code knowledge graph✅ LadybugDB✅ LadybugDBpartial❌partial
MCP server✅ 9 composite tools✅ 16 toolspartial❌❌
Per-edge confidence taxonomy✅ EXTRACTED / INFERRED / AMBIGUOUS❌❌❌❌
Git archaeology as a first-class layer✅❌❌❌partial
Durable committed markdown artifacts✅ 5 filespartialpartial✅ (wiki)❌
Agent-insight layer (record_insight / recall_insights)✅❌❌❌❌
Multi-platform skill emission✅ 10 shimspartialpartial❌❌
Local-only (no cloud required)✅ co-equal✅✅❌❌

GitNexus is the runaway leader in this category — but its PolyForm Noncommercial license locks every commercial user out. forensic-deepdive is the Apache-2.0 alternative: same persistent-graph-plus-MCP shape, plus honest confidence tags, git archaeology, persistent agent memory, and the 5 markdown artifacts as a fallback for any agent that doesn't speak MCP — with a license your employer's legal team will actually sign off on.


What it produces

forensic-deepdive analyzes any codebase (9 languages, polyglot) and produces:

  1. A persistent embedded graph at /.deepdive/graph.lbug — File, Symbol, Module, Commit, Author, Endpoint, and DbTable nodes plus DEFINES, MEMBER_OF, IMPORTS, CALLS, EXTENDS, IMPLEMENTS, TOUCHED_BY_COMMIT, AUTHORED_BY, CO_CHANGES_WITH, and the cross-boundary HANDLES / CALLS_ENDPOINT / ROUTES_TO / INJECTS / PERSISTS_TO edges. Every edge carries a confidence tag (EXTRACTED / INFERRED / AMBIGUOUS) — no hidden heuristics. The single Endpoint join node unifies five cross-boundary protocols (HTTP, MCP tools, registry-dispatch, gRPC, messaging/AMQP), so a frontend call resolves to its backend handler across the stack as one ROUTES_TO edge.
  2. An MCP server (forensic serve) exposing 9 composite tools (impact, context, archaeology, flow, query, record_insight, recall_insights, visualize, trace) consumable by Claude Code, Cursor, Codex, Continue, Cline, Windsurf — and any other MCP-aware agent.
  3. Five durable markdown artifacts under /docs/codebase/, regenerated from the graph on every extract:
  • `MAP.md` — what's where, ranked by centrality.
  • `HOTPATHS.md` — the dependency hot spots, with a per-row confidence-mix column so you see exactly how cleanly each symbol resolves.
  • `ARCHAEOLOGY.md` — why the code looks the way it does (git history, top authors with %, bus factor, co-change clusters, defect proximity).
  • `MENTAL_MODEL.md` — the doc the original author *would* write to onboard a new hire.
  • `AGENT_BRIEF.md` — ≤5 KB of assertive Never/Always rules with per-rule confidence tags. Drop-in CLAUDE.md for any project.
  1. Ten shims into the target repo — 4 editor rule files (CLAUDE.md, AGENTS.md, .cursor/rules/codebase.mdc, .continue/rules/codebase.md), 5 single-intent Claude skills under .claude/skills/codebase-{exploring,debugging,impact-analysis,refactoring,onboarding}/, and a .claude-plugin/plugin.json manifest. All write-if-absent — hand-edited files are never overwritten.
  2. An agent-insight layer — record_insight / recall_insights MCP tools backed by /.deepdive/insights.jsonl by default (zero dependencies, human-readable, git-friendly). The optional [graphiti] extra upgrades to a temporal knowledge graph backend above a 2-of-5 repo-size threshold.

Extract also regenerates `ARCHITECTURE.md` — a system-level Mermaid view of the cross-boundary graph (ROUTES_TO / INJECTS / PERSISTS_TO, confidence-styled), a *separate human-validation surface* (not one of the five contract artifacts, exactly like forensic visualize and serve --ui). Regenerate it on its own with forensic diagram --repo . Use it to sanity-check the graph — a wrong edge there is a wrong edge everywhere.

Add `--emit-vault` to also write an Obsidian-friendly vault under /vault/ — every artifact gets summary:/tags: frontmatter, cross-references become [[wikilinks]], and an INDEX.md MOC ties them together (with a .obsidian/ config). A local-first second brain for humans (graph view, backlinks) and agents (triage by summary: without opening files, a traversable index). Opt-in; off by default.

Status

v0.10.0 "The Upgrade Path" — an integrity release. v0.9 shipped correct, and then its findings run caught --refresh-shims being structurally unable to refresh half its targets, with 909 tests green over it. The cause wasn't the fix — it was that **every test in the suite wrote into an empty directory, so only the *first* run had ever been tested.** Everyone who isn't a new user lives on the untested path.

So 0.10 adds almost no surface. It makes the guarantees already made real: running Deepdive over a repo that already carries a previous release's output now provably converges (and provably leaves your hand-edited files alone), extract tells you when generated shims are stale instead of waiting for you to know the flag exists, and a stale examples/ fails CI rather than being caught by eye mid-release. New: forensic extract --timings, per-phase wall clock.

It also contains a cancelled feature, which is the more useful story. The planned performance work targeted PageRank. The profile required before touching it measured PageRank at 0.04 % of a large extract, and the *already-batched* store writes at 79–87 %. The optimization was disproven before it was written — see docs/findings/v0.10/PROFILE.md. Confirming a known technique had been applied had been mistaken for confirming it was fast.

The engine, the graph, the contract layer and the 5-artifact + 9-MCP-tool contract are unchanged from 0.9, and emitted content is byte-identical but for the version footer.

⚠ If you installed 0.8.0 or 0.9.0 and `forensic serve` stopped working — this is why. Those

releases declare mcp>=1.27.1 with no upper bound. mcp 2.0.0 removed mcp.server.fastmcp,

which the MCP server imports, so a fresh install of 0.8.0 or 0.9.0 today resolves to mcp

2.0.0 and fails on import. Our development lockfile pinned 1.27.1, so every local check stayed

green and only the clean-environment wheel smoke in CI could see it. Fix: upgrade to 0.10.0

(which caps mcp<2), or pin mcp<2 yourself. Nothing else about those releases is affected.

What's proven, and what isn't — the honest-framing section moved to docs/findings/HONEST.md (unchanged content, DEC-119). Short version: proven usable with real agent auto-discovery; not proven to make autonomous issue resolution measurably faster end-to-end (hardware-gated). This is an assisted-analysis tool, and it is not overclaimed as more.

Quick start

# install from PyPI (puts `forensic` on PATH); or run ephemerally with uvx

uv tool install forensic-deepdive forensic info # banner + capability panel forensic extract /path/to/repo

…or from source for development:

git clone https://github.com/Dhevenddra/forensic-deepdive && cd forensic-deepdive uv sync --all-extras

what can it do? (banner + capability panel: artifacts, protocols, MCP tools, confidence legend)

uv run forensic info

guided setup: analyze a repo, then wire it up as an MCP server

(--yes takes every default: scriptable, and the one mode that needs no extra)

uv run forensic onboard --repo /path/to/repo

run on any repo

uv run forensic extract /path/to/repo

graph lands at /.deepdive/graph.lbug

5 markdown artifacts at /docs/codebase/

10 shims at /.claude/, .cursor/, .continue/, root

trace a cross-stack feature slice (frontend call -> endpoint -> handler -> tail)

uv run forensic trace --repo /path/to/repo

interactive query REPL over one held-open store (needs the [interactive] extra)

bare text = natural-language query (no LLM) · :cypher = raw Cypher · :help · Ctrl-D exits

uv run forensic repl --repo /path/to/repo

full-screen terminal graph browser — the loopback-free sibling of serve --ui

1/2/3 = Symbols/Files/Endpoints · type to filter · c/e/l = confidence/edge/language · Enter = context · i/f = impact/flow

uv run forensic browse --repo /path/to/repo

the session shell: all of the above over ONE held-open graph, with history

in-session: extract · query · trace · impact · flow · diagram · browse · onboard · serve

uv run deepdive --repo /path/to/repo

query the graph as an MCP server (point it at the analyzed repo)

uv run forensic serve --repo /path/to/repo

inspect every repo you've analyzed

uv run forensic list

Install from PyPI

Published as [`forensic-deepdive`](https://pypi.org/project/forensic-deepdive/) — no clone needed:

uv tool install forensic-deepdive        # puts `forensic` on PATH

forensic extract /path/to/repo

…or run ephemerally, no install:

uvx forensic-deepdive extract /path/to/repo

Optional extras: uv tool install "forensic-deepdive[semantic]" (offline ONNX NL query), [interactive] (the forensic repl query console, the forensic browse TUI graph browser, and the deepdive session shell), [openapi] (YAML spec parsing), [graphiti] (temporal insight backend).

Upgrading from 0.8

Re-run extract with --refresh-shims once. The generated shims and skills under .claude/, .cursor/ and .continue/ are written only if absent, so a plain re-extract leaves your 0.8-era copies in place, and two of the skill files still cite internal decision IDs that 0.9 removed:

forensic extract /path/to/repo --force --refresh-shims

Only files Deepdive generated are rewritten. A shim you have hand-edited is never touched. pip install forensic-deepdive works too if you're not on uv.

Use it as an MCP server

forensic serve is a stdio MCP server exposing the 9 composite tools to any MCP-aware agent (Claude Code, Cursor, VS Code/Copilot, Codex, Continue, Cline, Windsurf). First build the graph once (forensic extract ), then wire the server. Three ways, easiest first:

1. Claude Code plugin (self-hosted marketplace — no PyPI step):

/plugin marketplace add Dhevenddra/forensic-deepdive

/plugin install forensic-deepdive@dhevenddra

2. From the [MCP Registry](https://registry.modelcontextprotocol.io) — indexed as io.github.Dhevenddra/forensic-deepdive, so registry-aware clients and discovery hubs (PulseMCP, MCPJungle, the VS Code @mcp index) can find and install it directly.

**3. Ma

Solo se muestra el principio; lee el resto en el repo

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