How do I use MCP Integration Bridge?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP Integration Bridge Map orders from my ERP to TMS and run a transfer" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

MCP Integration Bridge

by Randika97

Overview Schema Related Servers Score Discussions

Python

Hybrid

MCP Integration Bridge

An MCP server that connects any two systems so an agent can explore both, map between them, and run real data transfers — then shows its working in a run folder you can audit afterwards.

Nothing in the codebase names a product. Which systems the bridge talks to is decided by a .env file (addresses and credentials) and a profile (the shape of each system: its endpoints, entities, queries, and documentation tree). Pointing the bridge at a different pair of systems is a configuration change.

flowchart LR
  MCP["MCP client<br/>(Cursor, Claude Desktop)"] -- stdio --> B
  UI["Browser UI"] -- HTTP --> B
  B["Integration bridge"] <--> SRC["SOURCE system<br/>records read from"]
  B <--> TGT["TARGET system<br/>records written to"]
  B <--> HUB["HUB (optional)<br/>orchestration API"]
  ENV[".env"] --> B
  PROF["profiles/&lt;id&gt;/"] --> B

See docs/ARCHITECTURE.md for the full design.

What it gives an agent

Capability	How
Explore live APIs	Authenticated tools for each system: REST/OData reads, GraphQL queries, introspection
Explore the docs	Offline keyword search across each system's source or specification tree
Read named entities	`source_query_data` / `target_query_data` over operations the profile declares
Transfer records	`execute_workflow` runs forward, reverse, or a full round trip
Self-bootstrap	A missing workflow is built from a template, a codegen pipeline, or generated modules
Audit everything	Every tool call, payload, and skipped field lands in a timestamped run folder

Related MCP server: GAIIA Expert Proxy (MCP Server)

Quickstart

python -m venv .venv
.venv/Scripts/pip install -r requirements.txt   # Linux/macOS: .venv/bin/pip

cp .env.example .env      # then fill in the SOURCE_* and TARGET_* values
python scripts/selfcheck.py

selfcheck.py validates the profile, catalog, workflow and ingest wiring without contacting either system, so it works before you have credentials.

As an MCP server (stdio) — copy mcp.json.example into your MCP client config and adjust the paths:

.venv/Scripts/python server.py

As an HTTP API for a browser UI:

.venv/Scripts/python http_server.py     # http://127.0.0.1:8765

Start with bridge_info — it reports the active profile, both roles, and exactly which environment variables are still missing.

Configuration

Every connected system is a role, and all roles are configured the same way: <ROLE>_<OPTION>, where the role is SOURCE, TARGET, or HUB. Environment variables override the profile's defaults, so a profile ships the shape of a system and .env supplies the instance.

Minimum viable `.env`

ACTIVE_PROFILE=erp-to-tms

SOURCE_BASE_URL=https://your-tenant.example.com
SOURCE_TOKEN_URL=https://your-tenant.example.com/auth/realms/main/protocol/openid-connect/token
SOURCE_CLIENT_ID=your-client-id
SOURCE_CLIENT_SECRET=your-client-secret
SOURCE_DOCS_PATH=c:/Repositories/your-erp/workspace

TARGET_BASE_URL=https://your-platform.example.com
TARGET_USERNAME=you@example.com
TARGET_PASSWORD=your-password
TARGET_DOCS_PATH=c:/Repositories/your-platform

.env.example documents the full surface, including connector selection, path prefixes, login form field names, bootstrap behaviour, and the HTTP bridge.

Connectors

A role picks its protocol with <ROLE>_CONNECTOR:

Value	Authentication	Suits
`oauth2_rest`	OAuth2 client credentials → bearer	REST / OData service catalogues
`session_graphql`	CSRF-protected form login → cookie	GraphQL web applications
`token_api`	Credentials → JWT bearer	JSON APIs that issue a token from a login

Searching documentation

Each role's offline search is described entirely by settings, so it works against a source checkout, a specification bundle, or a folder of docs:

SOURCE_DOCS_PATH=c:/Repositories/your-erp/workspace
SOURCE_DOCS_GLOBS=*/model/**/*.projection,*/model/**/*.entity
SOURCE_DOCS_INDEX_GLOB=*/model/**/*.projection

TARGET_DOCS_PATH=c:/Repositories/your-platform
TARGET_DOCS_GLOBS=**/graphql/*.py,**/schema.py
TARGET_DOCS_INDEX_FILE=your_app/schema.py
TARGET_DOCS_INDEX_REGEX=(\w+Query)

DOCS_GLOBS selects what is searchable; the index settings produce the list of named API surfaces that *_search_docs returns alongside raw matches.

MCP tools

Bridge and runs

Tool	Purpose
`bridge_info`	Active profile, both roles, paths, and what is still unconfigured
`run_new`	Start a new run folder
`run_info`	Active run id, folder, and counters

Workflows

Tool	Purpose
`list_workflows`	Registered workflows and whether each is ready
`workflow_bootstrap_hints`	Search hints and a starter manifest for a missing workflow
`register_workflow`	Register a manifest plus `forward.py` and optional `reverse.py`
`bootstrap_workflow`	Build a workflow automatically
`execute_workflow`	Run `forward`, `reverse`, or `round_trip`

Source role — source_connection_info, source_search_docs, source_http, source_service_query, source_service_metadata, source_query_data

Target role — target_connection_info, target_search_docs, target_graphql, target_introspect, target_query_data

Hub role — hub_connection_info, hub_login, hub_trigger_run, hub_poll_run, hub_answer_run, hub_graphql, hub_http

Example agent flow

bridge_info                                        → confirm both roles are configured
source_search_docs("Shipment")                     → find the source API surface
target_introspect()                                → see what the target accepts
source_query_data("shipments", limit=5)            → sample real records
execute_workflow(workflow_id="booking", source_id="12345")
run_info                                           → the folder holding the evidence

Profiles

A profile describes one concrete pair of systems. profiles/erp-to-tms/ ships as a worked reference — copy it, edit it, and set ACTIVE_PROFILE.

profiles/<id>/
  profile.json     role defaults (connector, path prefixes, docs globs) + workflow manifests
  catalog.json     named read operations behind source_query_data / target_query_data
  ingest.json      write operations that generated mappings call
  reconcile.json   entities to compare across both systems
  smoke.json       the end-to-end pipeline and its pass criteria
  discovery.json   discovery step labels and the outcome catalogue
  templates/       bundled forward.py / reverse.py for bootstrap

Only profile.json is required.

Adding a readable entity

"source": { "operations": {
  "purchase_orders": {
    "description": "Purchase order headers.",
    "service": "PurchaseOrderHandling",
    "entity_set": "PurchaseOrderSet",
    "filter_template": "OrderNo eq '{identifier}'",
    "search_field": "Description"
  }
}}

source_query_data("purchase_orders", identifier="PO-1") works immediately, with no code change.

Adding a write operation

Generated mappings call methods that no Python file defines — the adapter resolves the name against ingest.json at call time:

"operations": {
  "create_order": {
    "document": "mutation CreateOrder($input: OrderInput!) { createOrder(input: $input) { order { id state } } }",
    "wrap_positional": "input",
    "variables": { "input": "{input}" },
    "root": "createOrder.order",
    "required": true,
    "flatten": { "id": "id", "state": "state" }
  }
}

Mapping logic too complex to express declaratively belongs in the workflow's forward.py / reverse.py, which is ordinary Python. That is the intended boundary: profiles describe what the systems offer; workflows describe how this business mapping works.

Workflows and runs

A workflow is a manifest (how to fetch from the source, what the target entity is called, whether a reverse callback exists) plus a forward.py and optional reverse.py. Asking for one that does not exist is not an error — the bridge tries a bundled template, then a codegen pipeline, then modules already generated under CODEGEN_ROOT, and only then returns needs_bootstrap with search hints and a starter manifest.

Every tool call runs inside a run folder:

runs/2026-08-11/143022-booking-12345/
  run.json  tool_calls.jsonl  transfer_log.jsonl  skips.jsonl
  inputs/   outputs/   mappings/

HTTP bridge

For browser UIs that cannot speak MCP stdio. Routes are role-shaped:

Method	Path	Purpose
GET	`/api/health`, `/api/info`	Liveness and active configuration
GET	`/api/workflows`, `/api/runs`	Registry and run history
POST	`/api/sessions`	Create a session
POST	`/api/sessions/{id}/configure`	Supply credentials
POST	`/api/sessions/{id}/configure-env`	Use the server's `.env`
GET	`/api/sessions/{id}/source/test`, `/target/test`	Prove each connection
GET	`/api/sessions/{id}/source/services`, `/target/types`	Discovered API surfaces
POST	`/api/sessions/{id}/discovery/start`	Walk both systems
POST	`/api/sessions/{id}/reconcile/run`, `/reconcile/lookup`	Master-data comparison
POST	`/api/sessions/{id}/smoke/run`	End-to-end proof
POST	`/api/sessions/{id}/agent/*`	Agent-planned discovery and builds

Repository layout

server.py                MCP entry point (stdio)
http_server.py           HTTP bridge entry point
integration_mcp/
  config/                .env loading, profiles, per-role settings
  connectors/            oauth2_rest, session_graphql, token_api, graphql_ingest
  catalog.py             profile-declared read operations
  search/                offline documentation and source-tree search
  transfer/              workflow execution
  workflows/             registry, manifests, bootstrap strategies
  runs/                  run folders, tool-call logging, spec snapshots
  http/                  sessions, discovery, reconcile, smoke, agent
profiles/                per-integration configuration
workflows/               registered forward/reverse modules (gitignored)
runs/                    run artefacts (gitignored)
scripts/                 selfcheck.py
docs/ARCHITECTURE.md     design, diagrams, extension points

Security notes

Each connector may only reach the hosts it was configured with. Add more with <ROLE>_ALLOWED_HOSTS, which keeps an authenticated connector from becoming an SSRF primitive.
Credentials are read from the environment only; .env and .env.* are gitignored. Cached cookies and tokens live under STATE_DIR.
*_connection_info tools report whether a credential is present, never its value.
Writing back to the source system is off by default; it requires post_callback=true or TRANSFER_POST_CALLBACK=1.

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MCP Integration Bridge

What it gives an agent

Quickstart

Configuration

Minimum viable .env

Connectors

Searching documentation

MCP tools

Example agent flow

Profiles

Adding a readable entity

Adding a write operation

Workflows and runs

HTTP bridge

Repository layout

Security notes

Maintenance

Resources

Looking for Admin?

Related MCP Servers

AX Local Operations MCP Server

GAIIA Expert Proxy (MCP

Enterprise MCP Server

agent-relay

Related MCP Connectors

Latest Blog Posts

MCP directory API

Minimum viable `.env`