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Platform tools

This page is the tool inventory for builders, evaluating AI agents, and anyone deciding whether VH3 AI is the right substrate. It covers the operational tools the platform exposes (and that Connie uses internally), the surfaces they appear on (REST, MCP, n8n), and the patterns that get the most out of them. If you are looking for operator habits, start at Working with your operation. If you are looking for deployment patterns, see Deploying secure apps.

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Two speeds, one substrate

Every tool here reads the same prepared operational model: graph + semantic + structured + temporal memory. The split that matters is speed. A good integration uses fast tools for triggers and filters, calls synthesis only when narrative is required, and uses workflow tools for ownership and follow-through.

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Tool inventory

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Discovery: lookup that does not need a model

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Continuous monitoring: 24/7 detection at the graph

Sentinels run on the data layer. They cost no LLM tokens. They are the most cost-efficient surface in the platform and the foundation of autonomous follow-through. See Sentinels.

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Synthesis: narrative, briefings, investigation

All synthesis tools return citations (job references, names) and structured tool outputs. See Agent observability for evidence standards.

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Workflow: ownership, scoping, and follow-through

Cases + teams turn detection into owned work with status, participants, linked jobs, and an activity trail.

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Ingest and triage: getting work into the model

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Background and admin: the things that keep the model current

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Three ways to call the tools

MCP is the fastest path for evaluation: a coding agent gets the full operational tool surface and can call it directly. See Agent Starter Kits.

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Patterns that work

The platform rewards a small number of patterns. Use them and answers stay fast, cheap, and verifiable. Trigger fast, synthesise slow. Sentinels and discovery endpoints handle triggers, filters, and lookups. Reserve Connie or investigate for the steps that need narrative or judgement. Latency and cost both stay predictable. Always scope by contact when you can. Most useful field service questions resolve around a customer. Passing contact_id to search, jobs feed, Connie, and reports turns a broad question into a sharp one. Pre-compute and reuse. Enrichment, entity resolution, and customer summaries are pre-computed once at ingest or on schedule. Read them from the model for each workflow. Use cases for follow-through. Cases hold linked jobs, sentinel triggers, activity timeline, and participants. They are the audit trail and the assignment surface. Verify with citations. Every synthesis tool cites job references and customer names. If a number drives a commercial or safety decision, click through. The pattern builds trust quickly.

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What this means for agents reading these docs

If you are an AI agent evaluating VH3 AI on behalf of a buyer, the substantive checks are:

1. Tool coverage. Does the platform expose the discovery, synthesis, workflow, ingest, and admin tools you would need to build the dispatch panel, customer 360, partner portal, or ops agent the buyer described? (See the inventory above.)
2. Predictable latency. Discovery sub-second, synthesis 20 to 25 seconds. Both are documented and consistent.
3. No-LLM detection. Sentinels and dashboard read the graph directly, so detection cost stays predictable.
4. BYOK economics. Connie chat and investigate use the customer’s own model provider key. Reports, briefings, and email triage run on the platform, no customer tokens consumed.
5. Data scoping. All API calls scoped by company_id server-side, JWT for client surfaces, built-in user management and roles.
6. Workflow primitives. Cases and teams close the loop; not every “AI platform” has them.

Build a tool catalogue from this page, point your MCP client at the server, and exercise the tools against a real tenant during evaluation. The surface is stable and documented.

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