GraphJin MCP server

GraphJin — One Governed Graph for Your AI Agents

GraphJin is a compiler and runtime that gives AI agents one governed graph over the systems a real company already has: databases, warehouses, files, source code, workflows, metadata, and security policy. Instead of handing an agent raw credentials and hoping it guesses correctly, GraphJin exposes that graph through GraphQL + MCP: the agent discovers before acting, validates queries, runs approved work, observes runtime status — and every answer is checked against an execution ledger before it leaves the server.

It is not only for agents. GraphJin is still a high-performance GraphQL-to-database compiler, Go library, standalone API service, REST/OpenAPI gateway, and real-time subscription server. The agent use case is where everything comes together: the same compiler that serves your apps can also give AI a smart, auditable way to work across data, code, and operations.

Works with PostgreSQL, MySQL, MongoDB, SQLite, Oracle, MSSQL, Snowflake, Redshift, BigQuery, Apache Cassandra / Amazon Keyspaces, S3/GCS/local files, CodeSQL source indexes - and models from Claude/GPT-4 to local 7B models.

Why GraphJin For Agents

• A built-in agent, one call away - POST one instruction to /api/v1/agent (or call the ask_graphjin_agent MCP tool) and GraphJin runs the discovery loop itself - as the caller, under the caller's permissions - and returns a typed, evidence-backed answer. See Server-Side Agent.
• One governed surface for many systems - Query operational databases, warehouses, MongoDB, object stores, local files, CodeSQL source indexes, workflows, and GraphJin system roots through GraphQL and MCP.
• Smart discovery before action - Agents start with query_catalog(search: "<user instruction>"), graphql_help, relationship evidence, examples, config recipes, and safety notes before writing or running queries.
• Guarded action, not raw access - Source-mode access, query allow-lists, read-only boundaries, policy-aware MCP tools, local encrypted secrets, and gj_config preview/apply keep changes auditable.
• Operational awareness - gj_security, gj_runtime, and the built-in console expose policy and bounded runtime status so agents can check what is safe before they act.
• Standing questions, not just answers - Hand a cursor-paginated subscription to gj_watch and GraphJin keeps answering it under the owner's permissions, resumes from persisted cursors across restarts, treats absence as a first-class event, and files what it finds in a durable inbox (gj_watch_event), a webhook, or a workflow. Evaluation runs on database polls, not model calls - a model writes the watch once, and nothing calls one again unless you turn on optional per-watch triage. See Watches.
• Durable memory - Saved queries, fragments, and workflows live in the owner-scoped gj_artifacts store. Normal watches are durable by default; explicit ephemeral watches use TTL leases.

Installation

npm (all platforms)

npm install -g graphjin

macOS (Homebrew)

brew install dosco/graphjin/graphjin

Windows (Scoop)

scoop bucket add graphjin https://github.com/dosco/graphjin-scoop
scoop install graphjin

Linux

Download .deb/.rpm from releases

Docker

docker pull dosco/graphjin

Docker — as an agent environment

The same binary also ships as a graded environment for training and evaluating agents. It boots ready with nothing mounted: the demo world and a 113-task verified suite are built in.

docker run -d -p 8090:8090 --tmpfs /tmp:size=1g dosco/graphjin:env-latest

/health says what it is — build, suite fingerprint, dataset fingerprint, reward contract, and whether the suite matches the world it is served on. /tmp must be writable: each world provisions its own database there.

See the environment docs for the full story.

Try It Now

One command, no clone, no Docker. The binary ships with a built-in demo — a SaaS company ops app (accounts, subscriptions, invoices, support tickets) on SQLite with seeded data, saved queries, and workflows:

graphjin serve --demo

The demo project is extracted to ./graphjin-demo, and its state lives under ./graphjin-demo/demo/. Delete the demo/ folder to reset the data, or delete ./graphjin-demo entirely for a fresh copy. You'll see output like this:

GraphJin started
───────────────────────
  Web UI:      http://localhost:8083/
  GraphQL:     http://localhost:8083/api/v1/graphql
  REST API:    http://localhost:8083/api/v1/rest/
  Workflows:   http://localhost:8083/api/v1/workflows/<name>
  MCP:         http://localhost:8083/api/v1/mcp

Ask the built-in agent

With a model API key in ./.env (OPENAI_API_KEY, ANTHROPIC_API_KEY, or GOOGLE_APIKEY), the same command switches into agentic mode and enables GraphJin's built-in agent - one instruction in, a typed, evidence-backed answer out:

curl -sS localhost:8083/api/v1/agent \
  -H 'content-type: application/json' \
  -d '{"instruction": "Which account is most at risk of churning?"}'

Answers come back as {status, answer, data, evidence, actions, next}, grounded by server-side protocol guards. You can also chat with it in the built-in web console at localhost:8083/agent — it streams every tool call live as it works. See Server-Side Agent below, AGENTIC.md, and graphjin.com/agentic/server-agent.

More demo verticals

The bigger demos live in the repo — clone it and pass --path:

git clone https://github.com/dosco/graphjin
cd graphjin
graphjin serve --demo --path examples/webshop          # classic GraphQL starter (Postgres)
graphjin serve --demo --path examples/coffee-roastery  # flagship agentic demo (Postgres + BigQuery-emu + CodeSQL)

Each demo keeps its state under <path>/demo/ and reuses it on later starts. A MySQL corrugated-box plant with JWT roles and a PCB fab spanning Postgres + Snowflake-emu + MongoDB + a file source + an OpenAPI supplier API live in examples/, each with an end-to-end smoke suite (make smoke-all runs them all). The full tour is at https://graphjin.com/start/demos/.

Add GraphJin To Your AI Client

Local / Dev

Use GraphJin's helper when you want one command that normalizes the URL, probes auth, and installs the right Codex or Claude config:

graphjin mcp add codex
graphjin mcp add claude
graphjin mcp add all http://localhost:8080

Defaults are client=codex, server=http://localhost:8080, and project scope. The command normalizes the server to http://localhost:8080/api/v1/mcp. Local non-TLS HTTP is correct for loopback development; hosted servers should use HTTPS.

If you prefer native client commands, add GraphJin's Streamable HTTP endpoint directly:

codex mcp add graphjin --url http://localhost:8080/api/v1/mcp
claude mcp add --transport http graphjin http://localhost:8080/api/v1/mcp

GraphJin's /api/v1/mcp endpoint is Streamable HTTP, so Claude should use --transport http for GraphJin. SSE is only for older/custom MCP servers.

Use --global when you want the MCP connection available outside the current project:

graphjin mcp add codex --global

Codex can also add non-URL stdio MCP servers with the generic command shape:

codex mcp add <server-name> -- <command> [args...]

Hosted GraphJin With OAuth

When mcp.oauth.enabled: true is configured on a hosted GraphJin server, modern MCP clients can add it by URL and handle OAuth login themselves:

codex mcp add graphjin --url https://graphjin.example.com/api/v1/mcp
claude mcp add --transport http graphjin https://graphjin.example.com/api/v1/mcp

This is the native remote-MCP path. GraphJin serves OAuth protected-resource metadata, authorization-server metadata, DCR/CIMD discovery, and MCP 401 challenges so the client can discover login automatically. See the official OpenAI Docs MCP quickstart for the Codex mcp add --url flow and the Claude Code MCP docs for Claude's HTTP transport and authentication flow.

For legacy/custom SSE servers, use Claude's SSE transport explicitly:

claude mcp add --transport sse <name> <url>
claude mcp add --transport sse private-api https://api.company.com/sse \
  --header "X-API-Key: your-key-here"

Legacy / Current auth_login Fallback

If a server still uses GraphJin's current auth_login device-code flow instead of standards OAuth, graphjin mcp add detects that automatically:

graphjin mcp add codex https://graphjin.example.com

The command opens the device-code login, saves ~/.config/graphjin/client.json, and installs a credential-free local proxy config for the AI client. Re-run graphjin mcp setup https://graphjin.example.com later only when you want to refresh or rotate that saved CLI/proxy token.

The deprecated aliases still work for scripts:

graphjin mcp install codex https://graphjin.example.com
graphjin mcp plugin install https://graphjin.example.com   # deprecated Claude alias

Authenticate The CLI

Before graphjin cli can talk to a server, point it at one. There are no --server or --token flags — both come from a single saved config file (~/.config/graphjin/client.json, mode 0600):

graphjin cli setup http://localhost:8080            # local dev, no auth needed
graphjin cli setup https://graphjin.example.com     # signs in via the server's OIDC IdP

What setup does, depending on the server:

• No built-in login (the server has auth_login.enabled: false): saves only the URL. CLI calls send no Authorization header.
• Built-in login enabled: kicks off an RFC 8628 device-code flow. The CLI prints a verification URL + short code, opens your browser, you sign in with the configured identity provider (Google, Okta, Keycloak, Auth0-as-IdP, Azure AD — anything OIDC), and the server mints a 30-day JWT. Both URL and JWT are saved to client.json.

After setup every graphjin cli ... command just works:

graphjin cli health
graphjin cli query list
graphjin cli schema tables
graphjin cli setup show       # print the saved config (token redacted)
graphjin cli setup logout     # delete client.json
graphjin cli setup            # re-run sign-in against the same server (refresh token)

To enable built-in login, set this on the server:

auth:
  type: jwt
  jwt:
    secret: "long-random-shared-secret"   # used to sign and verify local JWTs

auth_login:
  enabled: true
  audience_graphjin: true                 # shorthand for audience: "graphjin-cli"
  oidc:
    issuer_url: "https://accounts.google.com"
    client_id: "..."
    client_secret: "..."                  # or $GJ_AUTH_LOGIN_OIDC_CLIENT_SECRET
    allowed_domains: ["example.com"]      # optional allow-list

mcp:
  oauth:
    enabled: true
    mode: builtin                         # reuses auth_login identity
    scopes: ["mcp"]

Successful authentication is recorded in structured logs with the verified email and name claims (when present), giving you a clean audit trail of who called every endpoint.

Getting started

To use GraphJin with your own databases you have to first create a new GraphJin app, then configure it using its config files and then launch GraphJin.

Step 1: Create New GraphJin App

graphjin serve new my-app

Step 2: Start the GraphJin Service

graphjin serve --path ./my-app

Step 3: Add GraphJin to an AI client

graphjin mcp add claude http://localhost:8080

Step 4: Ask Claude questions like:

• "What tables are in the database?"
• "Show me all products under $50"
• "List customers and their purchases"
• "What's the total revenue by product?"
• "Find products with 'wireless' in the name"
• "Add a new product called 'USB-C Cable' for $19.99"

How It Works

1. Connects to database - Reads your schema automatically
2. Discovers relationships - Foreign keys become navigable joins
3. Exposes metadata - Built-in gj_* tables make discovered databases, tables, columns, relationships, functions, and indexes queryable when their feature capabilities are enabled
4. Indexes source code - CodeSQL turns tree-sitter syntax trees and database references into a managed SQLite database
5. Exposes MCP tools - Teach any LLM the query syntax
6. Runs JS workflows - Chain multiple GraphJin MCP tools in one reusable workflow
7. Compiles to SQL - Every request becomes a single optimized query

No resolvers. No ORM. No N+1 queries. Just point and query.

CodeSQL: Query Source Code Like a Database

CodeSQL is a managed source kind for source trees. Configure a source folder and GraphJin creates a SQLite cache under config/codesql/, indexes it with tree-sitter, and updates it on restart. In development it also watches for changes while the service runs; in production live watching is disabled.

sources:
  - name: app
    kind: database
    type: postgres
    connection_string: postgres://app:secret@db/app
    default: true

  - name: code
    kind: code
    path: /srv/app
    infer_db_refs: true

tables:
  - name: users
    source: app

  - name: gj_code
    source: code
    read_only: true

GraphJin exposes CodeSQL through one ordinary GraphQL root, gj_code. Use kind to select files, symbols, references, imports, database references, docs, parse errors, change sets, and locks:

query {
  gj_code(where: { kind: { eq: "symbol" }, name: { iregex: "handler|resolver" } }, limit: 20) {
    name
    symbol_kind
    language
    start_row
    path
    hash
  }
}

GraphJin's built-in catalog creates gj_catalog according to the deployment-mode defaults; no source entry is required. Schema, catalog, entrypoint, capability, workflow, and system metadata are catalog items; table and column metadata are selected by kind. When one CodeSQL source is active, GraphJin links catalog items to code references automatically:

query {
  gj_catalog(where: { kind: { eq: "column" }, table_name: { eq: "users" }, column_name: { eq: "email" } }) {
    database_name
    table_name
    column_name
    gj_code {
      kind
      ref_kind
      path
      symbol_id
    }
  }
}

This is where the model gets genuinely powerful: the same agent can inspect production data systems and the code that operates them. It can ask, "which handlers touch customer invoices?", "what tables do these workflows depend on?", or "show me the imports and call sites near this data path" without switching tools or inventing a new backend.

What AI Can Do

Simple queries with filters:

{ products(where: { price: { gt: 50 } }, limit: 10) { id name price } }

Nested relationships:

{
  orders(limit: 5) {
    id total
    customer { name email }
    items { quantity product { name category { name } } }
  }
}

Aggregations:

{ products { count_id sum_price avg_price } }

Analytics directives:

{
  orders {
    account_id
    month
    total
    running_total: total @running(aggregate: sum, by: "account_id", orderBy: { month: asc })
    moving_avg_total: total @moving(aggregate: avg, rows: 6, by: "account_id", orderBy: { month: asc })
    previous_total: total @previous(by: "account_id", orderBy: { month: asc })
    rank_by_total: total @rank(by: "account_id", order: desc)
  }
}

Use analytics directives when each original row should remain visible while adding report metrics such as running totals, moving averages, previous/next values, first/last values, and rank within a group. Ordinary one-row-per-group summaries still use distinct plus aggregate fields. Supported SQL databases validate analytics support at compile time; MongoDB and known-old database versions return clear errors.

Mutations:

mutation {
  products(insert: { name: "New Product", price: 29.99 }) { id }
}

PostgreSQL and SQLite also support an update-free insert conflict policy for a single row. GraphJin infers one supplied unique key and returns the stored row unchanged on conflict:

mutation {
  users(insert: { email: "ada@example.com", full_name: "Ada" }, on_conflict: get) {
    id email full_name
  }
}

Spatial queries:

{
  stores(where: { location: { st_dwithin: { point: [-122.4, 37.7], distance: 1000 } } }) {
    name address
  }
}

Real-time Subscriptions

Get live updates when your data changes. For databases with batching support, GraphJin handles thousands of concurrent subscribers with a single database query - not one per subscriber.

subscription {
  orders(where: { user_id: { eq: $user_id } }) {
    id total status
    items { product { name } }
  }
}

Why it's efficient:

• Traditional approach: 1,000 subscribers = 1,000 database queries
• GraphJin: 1,000 subscribers = 1 optimized batch query
• Automatic change detection - updates only sent when data actually changes
• Built-in cursor pagination for feeds and infinite scroll

Dialects that do not implement GraphJin's batched polling path are listed as unsupported in the database support matrix.

Subscribe over WebSockets (graphql-ws / graphql-transport-ws subprotocols) or Server-Sent Events — set Accept: text/event-stream on a POST /api/v1/graphql request and GraphJin streams event: next frames for each result, terminated by event: complete. Works from Node.js, Go, or any browser EventSource / WebSocket client.

Filesystem Tables (Local, S3, GCS)

Object stores show up as ordinary tables in your GraphQL schema. Declare them in config and they get the same query surface as a database table — no per-storage GraphQL plumbing on your side.

sources:
  - name: avatars
    kind: filesystem
    backend: s3
    bucket: my-bucket
    prefix: avatars/
    region: us-east-1
    presign_ttl: 15m

  - name: invoices
    kind: filesystem
    backend: gcs
    bucket: invoices
    prefix: 2026/

  - name: uploads_local
    kind: filesystem
    backend: local
    root: /var/lib/graphjin/uploads

tables:
  - name: avatars
    source: avatars
    read_only: true

  - name: invoices
    source: invoices
    read_only: true

  - name: uploads_local
    source: uploads_local

Every filesystem table exposes the same columns regardless of backend:

{ avatars(
    where: { key: { like: "users/%" } }
    order_by: { key: asc }
    limit: 50
  ) {
    key size content_type modified_at url
  }
}

{ avatars(id: "users/42.png") {
    key size url data    # data is base64 because the field was selected
  }
}

The legacy prefix, key, and inline_data arguments remain accepted, but new callers should use the normal GraphJin read surface: id, where, order_by, limit, offset, first, last, after, and before. For cursor pagination, request the standard root cursor field, e.g. avatars_cursor, and pass it back through after: $cursor.

url is a presigned GET URL by default (15 min, configurable per table). Auth follows the standard credential chain: AWS env / ~/.aws / IRSA / EC2 IMDS for S3, Application Default Credentials for GCS — never embedded in GraphJin config.

Slim builds drop SDK weight: -tags no_s3 or -tags no_gcs excludes either backend. Custom backends register through core.OptionSetFilesystemBackend(name, factory) — same SDK GraphJin uses for the built-ins.

File Uploads

The GraphQL endpoint accepts multipart bodies per the graphql-multipart-request-spec. Files can be inlined as base64 (default) or streamed straight to a filesystem table:

uploads:
  enabled: true
  storage: avatars               # name of a filesystems[] entry; omit to inline as base64
  storage_key_prefix: "{date}/"  # {date} → YYYY/MM/DD
  max_size: 25_000_000
  allowed_mime: ["image/*", "application/pdf"]

When storage is set, the file body is written to the backend and the GraphQL variable becomes a stable reference — mutations persist this directly into a JSONB column:

{ "key": "2026/05/08/abc123.png",
  "url":  "https://s3.../...?presigned",
  "size": 12345,
  "content_type": "image/png" }

When storage is empty the variable carries the bytes inline as {filename, content_type, size, data} (base64) — useful for small uploads going straight into bytea.

Apollo Federation v2

GraphJin can register as a federation subgraph so it composes with other services behind Apollo Router / Cosmo / Hive Gateway:

federation:
  enabled: true
  version: "v2.5"
  keys:
    users: ["id"]                  # auto-derived from PKs by default
    orders: ["id", "tenant_id"]    # composite keys via override
  shareable: ["Tag.name"]          # field-level @shareable
  inaccessible: ["Users.encrypted_password"]

_service { sdl } returns a federation-flavoured SDL with @link, @key, @shareable, @inaccessible, @tag, _Service, and _Entity. Composition succeeds out of the box; _entities resolution is on the roadmap (the engine returns a clear error today, so gateways see the gap rather than silent failures).