Iron Bridge Construction MCP Equipment Safety Server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Iron Bridge Construction MCP Equipment Safety ServerCheck the certification status of worker ID 447."
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.
Iron Bridge Construction — MCP Equipment Safety Server
Company & Problem
Iron Bridge Construction manages heavy equipment (cranes, excavators, scaffolding) across multiple job sites.
Before this system, site workers used paper checklists. Nothing stopped an uncertified operator from taking a crane, and nothing forced a human supervisor sign-off when the work was near power lines.
The fix: an MCP server that gives an LLM scoped, safe access to equipment data. The model never talks to the database directly. Every write that carries real risk is gated by capability checks, role changes, elicitation, and sampling.
Related MCP server: AgentGuard MCP Server
Protocol Concerns Mapping
# | Concern | How it appears in this system |
1 | Capability negotiation | Server checks |
2 | Notifications | Worker starts with read-only tools; |
3 | Elicitation |
|
4 | Resources | Safety policies ( |
5 | Prompts | Reusable template |
6 | Sampling | Before final approval of a high-risk request the server asks the client's model to draft a short risk summary; result is stored in audit log. |
7 | Progress tracking |
|
8 | Defensive tool design | Strict JSON Schema ( |
Transport
Development: stdio (default).
Demo / multi-site: Streamable HTTP (
MCP_TRANSPORT=streamable-http).
Commit history shows the transition from stdio-only to HTTP support.
Quick Start
python -m venv .venv
source .venv/bin/activate # or .venv\Scripts\Activate.ps1 on Windows
pip install -r requirements.txt
cp .env.example .env # set SUPERVISOR_PIN; ANTHROPIC_API_KEY is optional
python db/init_db.py
python agent/client.py # starts stdio server automaticallymcp is pinned to 1.29.0 in requirements.txt — mcp>=1.1.0 would let a
fresh install pull mcp==2.0.0, which removed the fastmcp module this
server is built on.
HTTP mode:
export MCP_TRANSPORT=streamable-http
export MCP_HOST=127.0.0.1
export MCP_PORT=8000
python -m mcp_server.server
# endpoint: http://127.0.0.1:8000/mcpTool Comparison
Tool | Read/Write | Needs elicitation? | Needs sampling? | Notes |
| read | no | no | always available |
| read | no | no | always available |
| write | yes if high-risk | yes if high-risk | creates PENDING request |
| write (session) | no | no | triggers |
| write | no | no | only after supervisor auth |
| read (report) | no | no | progress updates |
If a client connects without elicitation or sampling capability, high-risk requests return NOT_SUBMITTED and nothing is written to the database.
Folder Layout
db/ schema, seed, ERD, init script
mcp_server/ server, schemas, service layer, database helpers
agent/ demo client that performs the full handshake
docs/ progressive code parts for team commits
issues/ ready-to-paste GitHub Issue bodies (one per concern)Team Workflow (4 sequential parts per concern)
See docs/ and issues/. Each concern is an independent GitHub Issue.
Code for each concern is delivered in 4 progressive commits so every teammate has a visible contribution.
This server cannot be deployed
Maintenance
Related MCP Connectors
Supervised API-write gateway for AI agents with policy, human approval and execution receipts.
Runtime permission, approval, and audit layer for AI agent tool execution.
Zero-secret MCP gateway for AI agents: risk-scored, audited calls with human-in-the-loop approval.
The trust harness for AI agents. Set what an agent can do before it acts.
Related MCP Servers
- AlicenseNot gradedqualityBmaintenanceGates agent tool execution with human approval, audit trails, and replay-resistant permits, enabling safe use of tools in agent loops.MIT
- FlicenseNot gradedqualityBmaintenanceProvides a secure MCP boundary for AI agents, intercepting and validating tool calls, redacting secrets, and requiring human approval for sensitive actions with a tamper-evident audit trail.-
- FlicenseNot gradedqualityCmaintenanceEnables controlled AI-agent access to enterprise-shaped tools with a deny-by-default gated write path, human approval, dry-run execution, and append-only audit logging.1-
- AlicenseNot gradedqualityCmaintenanceEnables secure support-ticket and customer-account operations with signed JWT authentication, prompt-injection and tool-poisoning guardrails, and human-in-the-loop confirmation for destructive actions.MIT