Give your AI agent a verification tool (MCP)
The problem
AI agents hallucinate when interpreting complex documents, and cannot verify the authenticity of the files they ingest. An agent that reads a forged payslip or an invented citation does not just err — it acts on the error, at machine speed.
The failure stays invisible until a human checks the paper trail, which is usually after the decision has been made.
How Stipple helps
One hosted MCP server connects an agent to forensic document analysis, table extraction and citation verification natively. The agent calls verification tools the way it calls any other tool, with typed schemas and deterministic results.
No SDK and no scraping: streamable HTTP, zero install. The agent gets structured findings it can reason over and cite in its own audit trail.
The call
View Full Recipe →{
"mcpServers": {
"stipple": { "type": "http", "url": "https://www.stipple.sh/mcp" }
}
}Equip autonomous coding and workflow agents with verified document intelligence.
What the result looks like
The same typed results the REST API returns: risk band, per-signal severity and evidence traces, confidence bounds, a recommended action, and an audit-trail link.
An agent can branch deterministically on that. Low risk flows through; elevated routes to a human with the findings attached. Signals say what they tested and what they found, so the agent's own justifications stay grounded in something checkable.
The documented limits carry over unchanged: a clean result is coverage, not a certificate, and the quality fields tell the agent when a scan or photo degraded the signals — which is exactly when an agent should stop rather than proceed confidently.
Questions
- How do I add document verification to an AI agent?
- Add one entry to your MCP config pointing at the hosted server, and the agent lists the available verification tools natively. It works with any streamable-HTTP MCP client. Sandbox testing works without a key.
- What can the agent do with a result?
- Branch on it: proceed, extract, route to a human, or stop. Because findings include evidence traces, the agent can also explain its routing in its own audit trail, which is what makes an automated decision reviewable after the fact.
- Which tools does the MCP server expose?
- Document verification for payslips, bank statements, invoices and identity packs; table and line-item extraction; citation and reference verification; and AI-text detection. The task playbooks carry a code recipe and verified schema for each.
- Does the agent ever decide alone?
- By design, no. Recommended actions route consequential calls to a human with the findings highlighted. The philosophy is the same as the API's: a signal for your reviewer, not a black-box verdict.