Running Scry as a service, Research Tools

Scry Agency Implementation, Programmatic Research Delivery

Learn how to build research-as-a-service offerings using Scry's SQL-like query interface and standing queries. This course teaches agencies to deliver competitive intelligence, market research, and lead generation at scale by automating document retrieval across 43 sources and integrating live data streams into client workflows.

Open the decision record for Scry

What does running Scry for clients commit you to?

Published figures for this service. Blank fields are not published.

Monthly tool cost
Vendor plan pricing is not published in the supplied data; confirm Scry usage-based costs directly with the vendor before scoping client engagements. Setup complexity is medium and time to value is hours.
Time to first value
Not published
Payback
Not modeled
Guided implementation
8 hours

Is Scry worth running as a client service?

Scry provides an evidence-backed programmatic research capability with published benchmark performance and MCP-based accessibility, but vendor plan pricing, client fees, labor rates, usage volume, and overhead are all absent from the supplied data, so no ROI can be modeled. The agency must supply its own client pricing, delivery labor estimates, and expected query volume before an investment case can be made.

An agency-fit judgement for reselling this service. It is separate from the tool description on the decision record.

Before you start

What has to be in place before the first client engagement.

Tools and subscriptions

  • MCP client such as ChatGPT, Claude, Claude Code, Codex, or Cursor
  • Scry HTTP API key from dashboard for agents without an MCP client
  • Access to https://mcp.scry.io MCP server endpoint
  • Scry account with active subscription (vendor plan pricing not published in supplied data)
  • Scry dashboard access for API key management and usage monitoring

People and inputs

  • Technical staff capable of writing SQL-like query programs with relations, filters, joins, and vector compositions
  • Capacity to review live schema documentation including relation contracts, columns, indexes, extent, and known holes
  • Process for embedding text and expressions via the /v1/scry/embed endpoint when vector similarity search is required
  • Onboarding process compatible with medium setup complexity and hours-to-value timeline

Included with the course

7 working documents for delivering this service.

  • Scry Query Templates for Common Agency Projectstemplate
  • Standing Query Setup and Monitoring SOPsop
  • Competitive Intelligence Report Automation Checklistchecklist
  • MCP Integration Worksheet for AI Agent Setupworksheet
  • Scry Pricing and Capacity Planning Guideguide
  • Client Onboarding Workflow for Research Retainerssop
  • Vector Search Use Cases and Implementation Patternsguide

Listed by name. These documents are not yet published as individual downloads.