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Real estate · MCP server

Pedra: MCP server, setup and security

Pedra is an open-source MCP server that lets an AI agent edit real estate photographs and generate videos of listed properties. Its repository has 1 stars and its last commit is from October 16, 2026 and it is licensed under MIT.

Score
60/100
Adoption (stars) 0/40
Maintenance 30/30
Framework (licence, registry, official) 30/30
OfficialActively maintainedNeeds secrets

What Pedra does

According to its repository: “AI real estate photo and video editing via the Pedra API: virtual staging, renovation, room emptying, enhancement, sky replacement and property videos.”

An MCP server gives an AI agent extra tools it calls during the conversation: the agent reads their description, decides to use them and gets the result back. Pedra is listed under Real estate, a category of 6 servers in the directory, where it ranks #5 by stars.

Topics declared by the repository: claude, cursor, image-editing, pedra, proptech, real-estate.

Installing and connecting Pedra

Distribution modes declared in the official MCP registry:

  • npm package (Node.js, usually started with npx)

Like any MCP server, you declare it in your client's configuration (Claude Desktop, Claude Code, Cursor, VS Code, Codex…): a name, the command that starts it or its URL, and any environment variables. The client then starts it and lists its tools. Project language: TypeScript. Listed systems: macOS, Windows, Linux.

The registry lists one secret variable to provide (API key, access token). Keep them in the client's environment variables, never in a shared file or a repository.

Security: what to check

An MCP server runs code with the rights you give it, and the model reads its tool descriptions: a malicious or compromised server can hijack the agent. Read the code, or pick a maintained and widely used server, before connecting it.

Points found for Pedra:

  • Secrets: the server acts with your credentials. Create a dedicated key with minimum rights and an expiry date.

Good practice: grant only the rights needed (least privilege), require human approval for irreversible actions, and run the AI agent security check.

Project activity and maintenance

Created on June 23, 2026, the pedra-ai/pedra-mcp repository has 1 stars and 0 forks, with 1 open issues. It has more stars than 7% of the servers in the directory. It received changes this month. Version published in the registry: 0.6.1 (October 16, 2026).

Stars measure attention, not quality: a recent, heavily promoted project can collect them fast, while a useful but niche server will have few.

AbellAbell : 444444HasData RedfinHasData Redfin : 1010HasData ZillowHasData Zillow : 1010Propstack MCPPropstack MCP : 99PedraPedra : 11Addressintel MCPAddressintel MCP : 00
GitHub stars of the most followed servers in the category (log scale); Pedra in orange. Checked October 25, 2026.

Verdict

Pedra is a reasonable choice; check the points below before connecting it.

Strengths

  • Maintained by the service's own vendor
  • Active project: recent commit
  • Listed in the official MCP registry
  • Permissive licence (MIT)

Limitations

  • Requires an API key or token
  • Few visible users

Avoid if…

  • You do not want to give credentials to an agent

Frequently asked questions

How do I install Pedra?

Pedra is distributed as npm package (Node.js, usually started with npx). Add it to your client's MCP configuration (Claude Desktop, Cursor, VS Code…) following the README of pedra-ai/pedra-mcp.

Is Pedra free?

The code is published under the MIT licence and can be used free of charge. It needs an API key: the service it connects to may be paid.

Is Pedra safe?

Points to watch: secrets. Give it minimum rights and approve sensitive actions.

Is it an official server?

Yes: the awesome-mcp-servers list marks it as the service vendor's official implementation.

What are the alternatives to Pedra?

In the same category: HasData Redfin, HasData Zillow, Propstack MCP, and Addressintel MCP.

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Neutrality. No affiliate links. No vendor pays to be reviewed, rated or ranked. Reviews are directed by François Chollet, neutral and factual, and every data point is dated and sourced. How we score