Context7
Up-to-date docs for AI agents
Descripción del creador (original en inglés)
Persistent code knowledge graph + 9 composite MCP tools for AI coding agents.
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
Necesitas
¿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 .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.
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.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 6 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub Dhevenddra, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
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.
El registro no indica ninguna clave obligatoria, aunque algunos servidores te piden iniciar sesión la primera vez que los usas.
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).
El repo de GitHub cambió por última vez –, y la última versión (0.10.0) llegó al registro hace 3 meses.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Python (uv) en tu computadora.
En la misma categoría: Context7, Codebase Memory, Exa
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
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
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` 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.
| forensic-deepdive | GitNexus | CodeGraphContext | DeepWiki | Sourcegraph | |
|---|---|---|---|---|---|
| License | Apache-2.0 | PolyForm Noncommercial | MIT | proprietary (open variant: MIT) | partial |
| Persistent code knowledge graph | ✅ LadybugDB | ✅ LadybugDB | partial | ❌ | partial |
| MCP server | ✅ 9 composite tools | ✅ 16 tools | partial | ❌ | ❌ |
| Per-edge confidence taxonomy | ✅ EXTRACTED / INFERRED / AMBIGUOUS | ❌ | ❌ | ❌ | ❌ |
| Git archaeology as a first-class layer | ✅ | ❌ | ❌ | ❌ | partial |
| Durable committed markdown artifacts | ✅ 5 files | partial | partial | ✅ (wiki) | ❌ |
Agent-insight layer (record_insight / recall_insights) | ✅ | ❌ | ❌ | ❌ | ❌ |
| Multi-platform skill emission | ✅ 10 shims | partial | partial | ❌ | ❌ |
| 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.
forensic-deepdive analyzes any codebase (9 languages, polyglot) and produces:
/.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.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. /docs/codebase/, regenerated from the graph on every extract:CLAUDE.md for any project.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.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.
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>=1.27.1with no upper bound.mcp2.0.0 removedmcp.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 pinmcp<2yourself. 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.
# 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
git clone https://github.com/Dhevenddra/forensic-deepdive && cd forensic-deepdive uv sync --all-extras
uv run forensic info
uv run forensic onboard --repo /path/to/repo
uv run forensic extract /path/to/repo
uv run forensic trace --repo /path/to/repo
uv run forensic repl --repo /path/to/repo
uv run forensic browse --repo /path/to/repo
uv run deepdive --repo /path/to/repo
uv run forensic serve --repo /path/to/repo
uv run forensic list
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
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).
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.
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