mcpField
P

ProofStream

Local
@ProofStreamaiBusiness & Marketing

About ProofStream

Dispatch a human verifier to physically verify anything in the real world. For AI agents

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "proofstreamai-proofstream": {
      "command": "npx",
      "args": [
        "-y",
        "proofstream-mcp"
      ]
    }
  }
}
Run commandnpx -y proofstream-mcp

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npmproofstream-mcp0.1.1

Tools

3

Saved tool list · updated 2w ago

ParameterTypeRequiredDescription
namestringYesRequester full name
emailstringYesEmail for report delivery and credentials
billingenumNoone_time | enterprise
companystringNoOrganization name (optional)
metricsstringNoSpecific checkpoints or things to look for during verification
serviceenumYesproduct = Product Verification | document = Document Verification | property = Property & Asset Checkproduct | document | property
urgencyenumNostandard = 24-72 hours | rush = within 24h (+50%) | same_day = +100%standard | rush | same_day
locationstringYesPhysical address or location of the item/property to verify
livestreamenumNoWhether to include live video feed access (adds cost)yes | no
client_typeenumNoType of requesterAI Agent / Autonomous System | Enterprise / Business | Individual | Legal / Compliance | Insurance | Other
descriptionstringYesWhat needs to be verified — be specific
access_notesstringNoAccess instructions, contact info for site access
deliverablesarrayNoRequired deliverables (Timestamped photo report always included)
preferred_datestringNoPreferred verification date (YYYY-MM-DD)

Details

The maintainer has not added details yet. Check the README in the repository.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.