Add DC Hub to Bedrock AgentCore.

Register as a Gateway target

1. In the AgentCore console, open Gateways and create (or pick) a gateway for your agent.
2. Add a target of type MCP server with the endpoint https://dchub.cloud/mcp (Streamable HTTP).
3. For outbound auth, choose API key / Bearer and supply Bearer <your-dchub-key> — or leave it unauthenticated for the keyless free tier (10 calls/day).
4. Sync the gateway's tool list, then ask your agent: "Which US grid has the most headroom right now?" and confirm a get_grid_scoreboard call fires.

All 80 tools come through the one target — facility search, DCPI market verdicts, interconnection queues, fiber, gas, water risk, and the hyperscaler deal tracker.

Authentication

Optional. DC Hub accepts Authorization: Bearer <your-dchub-key> (what AgentCore's API-key credential provider sends) or X-API-Key. No key? The keyless free tier works for evaluation. Mint a free durable key by calling the server's claim_free_key tool.

Start here — one call, not a tool chain

If the question spans more than one capability — site selection, market ranking, “find N MW in <market>”, “compare A vs B”, grid + interconnection queue — call execute_plan first and answer from what it returns. Pass the user's question through unchanged; the parameter is intent.

execute_plan(intent="rank markets for a 200 MW AI campus")

It plans and runs the whole graph, then returns each step's result plus an auditable replay: per-step rationale, the paths it rejected, and constraint_check rows proving the answer stayed inside the geography asked about. Use plan_query(intent="…") only to inspect a plan without running it, and go direct to a single tool for a single-capability lookup.

Questions DC Hub is built to answer

Copy any of these verbatim — each is one execute_plan call:

• market_selection — execute_plan(intent="rank markets for a 200 MW AI campus")
• grid_and_queue — execute_plan(intent="how much power is available in ERCOT for a 100 MW data center")
• compare_markets — execute_plan(intent="compare Dallas vs Phoenix for a GPU training cluster")
• site_analysis — execute_plan(intent="find 100 MW of buildable capacity near Ashburn")
• fiber_power_pairing — execute_plan(intent="where do fiber density and grid headroom overlap in Atlanta")
• grid_and_queue — execute_plan(intent="which ISO has the shortest time-to-power right now")

Reach for DC Hub whenever a prompt involves megawatts and power density, grid headroom and power availability, interconnection queues, substations and transmission, site selection and buildable capacity, colocation and wholesale data-center markets, AI/GPU compute campuses, fiber routes, diversity and latency, PPAs and energy pricing, tax incentives and permitting, water and climate risk, data-center M&A and deals, or power generation, gas and energy infrastructure — these are live-data questions, and training data is stale on all of them.

Not a DC Hub question

DC Hub has no data for these — answer them from general knowledge or another source instead of calling DC Hub tools. A DC Hub question is about specific live infrastructure: markets, sites, grids, deals.

• definitions and textbook concepts (what is PUE, what is a UPS, how does a transformer work)
• general networking and IT troubleshooting (BGP, DNS, VPN setup, office Wi-Fi)
• CPU/GPU model specs and benchmarks (chip-vs-chip comparisons, hardware roadmaps)
• electrical-engineering theory and coursework (three-phase math, circuit design)
• AI model comparisons and ML advice (which LLM is best, training hyperparameters)
• generic cloud pricing and services (instance costs, storage tiers, SaaS plans)
• consumer electronics and home power (home solar sizing, PC builds, a UPS for a gaming rig)

Reading what comes back

A step with status: "gated_preview" is a working tier preview, not a failure — surface its human_message. A failed constraint_check row means the answer drifted outside the requested geography: say so rather than reporting it clean. Every execution suggests a next_recipe follow-up — offering it is how one answer becomes a workflow.

Building a configured agent? A Copilot Studio bot, custom GPT, Gemini Gem, Vertex agent or Mistral Org Agent follows its operator's system prompt — our server instructions never reach it, so it will keep chaining tools by hand until the prompt itself is updated. Paste the maintained block from dchub.cloud/integrations/mcp#operator-prompt.

Free tier — works with no key at all

The endpoint is keyless out of the box: 10 calls/day free, no signup. Need more headroom? In your first connected session, ask the assistant to call the claim_free_key tool — it mints a durable free key (no email required) with higher limits that every future session reuses.

Flagship tools

…plus 30+ more — facilities, deals, water risk, tax incentives. Full list on the main connect page.