Iron Bridge Construction — MCP Equipment Safety Server
MCP server for heavy equipment management with role-based access, supervisor authentication, and high-risk request elicitation/sampling.
README
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.
Protocol Concerns Mapping
| # | Concern | How it appears in this system |
|---|---|---|
| 1 | Capability negotiation | Server declares elicitation + sampling. Client checks before offering high-risk tools. |
| 2 | Notifications | Worker starts with read-only tools. After authenticate_supervisor, server pushes tools/list_changed and approval tools appear. |
| 3 | Elicitation | request_equipment for high-risk items (crane / near power lines) pauses mid-call and asks a human supervisor for explicit confirmation. |
| 4 | Resources | Safety policies (lifting-safety, electrical-proximity) are exposed as resources, not tools. |
| 5 | Prompts | Reusable template prepare-equipment-receipt that takes {request_id}. |
| 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 | generate_site_compliance_report walks every request and reports 25/50/75/100 %. |
| 8 | Defensive tool design | Strict JSON Schema (additionalProperties: false), server-side jsonschema validation, parameterized SQL, and handler-level authorization. |
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
python db/init_db.py
python agent/client.py # starts stdio server automatically
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/mcp
Tool Comparison
| Tool | Read/Write | Needs elicitation? | Needs sampling? | Notes |
|---|---|---|---|---|
check_worker_certification |
read | no | no | always available |
get_equipment_status |
read | no | no | always available |
request_equipment |
write | yes if high-risk | yes if high-risk | creates PENDING request |
authenticate_supervisor |
write (session) | no | no | triggers tools/list_changed |
approve_equipment_request |
write | no | no | only after supervisor auth |
generate_site_compliance_report |
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.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.