Context7
Up-to-date docs for AI agents
Beschreibung vom Entwickler (englisches Original)
Local knowledge graph memory MCP server for AI assistants.
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.
Der Entwickler hat in der Registry keine Installationsangaben gemacht. Sieh dir unten die README des Entwicklers an.
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 8. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto nnar1o 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 (0.3.0) kam vor 3 Monaten in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code.
In derselben Kategorie: Context7, Codebase Memory, Exa
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
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.
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.
Beta - APIs may still change and some bugs are still expected.
kg gives your AI assistant persistent, structured, editable project memory stored locally as a knowledge graph.
Instead of relying only on document chunk retrieval, you can keep architecture, decisions, incidents, rules, dependencies, and workflows in a graph that is readable, reviewable, and Git-friendly.
Use it when you want your assistant to understand an existing project across sessions — not start from zero every time.
*.kg files with readable diffsClassic RAG is good for retrieving text chunks from documents.
kg-mcp is better when you want:
cargo install kg-cli
Recommended install:
curl -sSL https://raw.githubusercontent.com/nnar1o/kg/master/install.sh | sh
You can also download a ready binary from GitHub Releases.
kg-mcp to Your AI ClientAdd kg-mcp as a local stdio MCP server.
Example config:
{
"mcpServers": { "kg": { "command": "/absolute/path/to/kg-mcp" } } }
After that:
kg MCP server is available,Full MCP setup and reference: docs/mcp.md
kg understands short, verb-first English commands (SCL — Simple Command Language). The active graph is resolved from your config automatically.
find "compressor defrost"
get concept:refrigerator add concept:smart_fridge --name "Smart Fridge" --description "Connected refrigerator" modify concept:smart_fridge --importance 0.9 remove concept:old_idea connect process:compressor_control TRIGGERS process:auto_defrost disconnect process:compressor_control TRIGGERS process:auto_defrost list nodes stats use fridge help
| Verb | What it does | ||||
|---|---|---|---|---|---|
find | search nodes by text | ||||
get | fetch one node by id | ||||
add --name "Name" | create a node (type inferred from id prefix) | ||||
modify --field value | update node fields | ||||
remove | delete a node | ||||
connect | create an edge (alias: add edge) | ||||
disconnect | delete an edge (alias: remove edge) | ||||
| `list nodes\ | edges\ | types\ | relations\ | graphs` | list graph contents |
stats | show graph statistics | ||||
use | switch active graph | ||||
help [verb] | get help for a verb or all | ||||
| `feedback yes\ | no\ | nil\ | pick ` | give feedback on search results | |
strict | disable defaults for following lines |
Format: :snake_case — e.g. concept:fridge, bug:door_seal, process:compressor_cycle.
HAS USES STORED_IN TRIGGERS CREATED_BY AFFECTED_BY AVAILABLE_IN DOCUMENTED_IN DEPENDS_ON TRANSITIONS DECIDED_BY GOVERNED_BY READS_FROM
; or newlines. Lines starting with # are comments.use to switch graphs within a script.kg node find ... commands still work as fallback.docs/scl.mdThis is the first workflow for a new project: ask the assistant to create or extend a graph from your documentation.
By default, graphs are stored in ~/.kg/graphs as *.kg files.
Minimal prompt:
You are connected to kg-mcp.
Project graph name: payments
Build or extend this graph from the project documentation I provide. Use payments as the graph name for all graph operations.
Only add facts grounded in source material. If an important fact is missing and can be inferred safely from the provided docs, update the graph. If something is ambiguous, ask or record it as a note instead of inventing facts.
Example prompt with documents:
Use kg-mcp to build or extend the `payments` graph from these documents:
Only add facts grounded in the documents. If something is ambiguous, keep it out of the graph or record it as a note. When you finish, summarize what was added, what remains unclear, and what document should be ingested next.
Longer prompt for this workflow: docs/ai-prompt-graph-from-docs.md
For a ready-made repository example, run cargo run --bin repo-example to generate repo-example.kg from this repo.
kg can turn an existing folder into a graph automatically. It scans the directory tree, recognizes many common file types, extracts symbols for Rust, Java, JavaScript/TypeScript, Python, and C/C++, and keeps the generated structure separate from the manual graph.
For markdown-like documents, it also creates document (GDOC) and chapter (GSEC) nodes with section content.
It is a fast way to get a useful map of a codebase or workspace without modeling everything by hand. The generated index is local, refreshable, and safe to ignore in git.
Example:
cargo run --bin repo-example
This generates repo-example.kg from this repository as a local demo.
Once the graph exists, the normal workflow is to ask the assistant to inspect it and answer questions from it.
Example prompt:
Use kg-mcp to inspect my existing `payments` graph.
I want to understand:
If the graph is missing critical information, say exactly what is missing.
Other useful questions:
payments graph?"You can also ask the assistant to improve the graph while you work.
Example prompt:
Use kg-mcp to review my existing `payments` graph.
Find:
Apply safe improvements where possible. Only add facts grounded in the graph, the provided docs, or the current discussion. If something is ambiguous, leave it out or add a note.
When you finish, summarize:
This works best when your main system prompt or project prompt already tells the assistant which graph belongs to the project.
Minimal project-level prompt:
You are connected to kg-mcp.
Project graph name: payments. Use this graph for relevant reads and updates in this project. If you notice important missing information that is grounded in the available docs or conversation context, update the graph as part of your work. If uncertain, ask or add a note instead of inventing facts.
.kg.toml)kg looks for .kg.toml in the current directory and its parent directories.
Example:
backend = "json" # json backend writes native .kg files by default
graph_dir = ".kg/graphs" graph_dirs = ["../shared-graphs", "../team-graphs"] nudge = 20 user_short_uid = "dev_01"
[graphs] payments = "graphs/payments.kg"
Notes:
backend = "json" is the default and prefers .kg text graphs.backend = "redb" stores graphs in .db files.graph_dir sets a primary graph directory.graph_dirs adds extra directories scanned by kg list and graph resolution.The default graph directory is ~/.kg/graphs.
You can put that directory under git.
Recommended approach:
*.kg graph files in git,Suggested .gitignore:
*.kglog
*.kgindex *.event.log *.migration.log *.bak *.bck.*.gz
In practice:
*.kg is the main graph file you usually want to review and commit,*.kglog is a local access/feedback log,*.kgindex is a generated local index,*.event.log is a local append-only change timeline,*.bak is the previous on-disk version from the last write,*.bck.*.gz are periodic compressed backup snapshots,*.migration.log is a migration report when older graphs are converted.*.kg is git-friendly and intentionally structured to make diffs readable and merges easier when several people work on the same graph.
To generate an interactive HTML view of a graph:
kg graph payments export-html --output payments.html
You can keep the generated HTML as a shareable visual snapshot of the current graph.
docs/mcp.md - MCP setup and tool referencedocs/ai-prompt-graph-from-docs.md - longer prompt for document ingestiondocs/build-graph-from-docs.md - graph-building workflow from docsdocs/troubleshooting.md - common issuesFor questions or feedback: nnar10@proton.me