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Sf Intelligence

Offline, read-only Salesforce org intelligence with metadata search, dependency analysis, impact checks, and cited answers.

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Install Sf Intelligence

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "sf-intelligence": {
      "command": "npx",
      "args": [
        "-y",
        "sf-intelligence"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Install not yet sandbox-tested— we haven't run this command through our automated sandbox yet. Not a red flag; we're working through the catalog.

Overview

sf-intelligence turns one Salesforce org's retrieved metadata into a local, read-only knowledge base for AI assistants. It stores raw metadata, component pages, and a dependency graph in a local vault, then answers questions offline through MCP tools. Use it to trace field usage, assess deletion impact, inspect save-order automation, find potentially unused Apex, or explain effective permissions. A built-in demo works without Salesforce login, while an optional live read-only connection can refresh metadata from an org.

Use cases

  • Trace every metadata and code reference to a Salesforce field
  • Assess whether deleting a Salesforce component could break dependencies
  • Inspect the automation order for a record-save event
  • Find Apex classes and triggers with no detected callers
  • Explain effective user visibility and edit permissions

Key features

  • Local offline vault for Salesforce metadata
  • Dependency graph with confidence-tagged relationships
  • Read-only refresh from a Salesforce org
  • Typo-tolerant component resolution with clarification prompts
  • Deletion-impact, automation, Apex, and permission analysis
  • Cited answers showing checked and unchecked metadata

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked Today

Recent health check succeeded.

19/21 checks healthy over the last 90d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 16h ago via OSV.dev · sf-intelligence (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars3Stargazers on the source repository.
npm downloads2.2kPackage downloads in the last 30 days.
Last commit19d agoMost recent push to the default branch.
Directory activity2 viewsConfig copies, upvotes, and views on AllMCPs.

How Sf Intelligence works

What sf-intelligence does

sf-intelligence gives AI assistants a local view of one Salesforce org's metadata, dependencies, and impact relationships. It is designed for questions where changing or removing a component could have consequences, such as finding every reference to a field, checking whether a field appears safe to delete, or determining what runs when a record is saved.

The server is read-only. It does not make changes to the Salesforce org, and its normal question-answering path works from a local vault rather than making network requests. Answers identify the components used as evidence and distinguish relationships found directly in metadata from those parsed from source or inferred by pattern matching.

How it works

A refresh retrieves Salesforce metadata through a read-only Salesforce project operation. The retrieved source is converted into a local vault containing raw metadata, one Markdown page per component, and a dependency graph backed by DuckDB. Subsequent questions are handled on the local machine.

An AI host first resolves the component referred to in a natural-language question. The resolver tolerates misspellings and asks for clarification when multiple components could match instead of silently choosing one. The host then calls a focused analysis tool or routes the request through the general analysis gateway. Results include cited components, confidence information, and the metadata families that were or were not checked.

An optional live plane can be enabled for read-only access to the org. It is off by default, and the local vault remains the source for offline analysis. A deterministic concept model can add structural Salesforce explanations, such as master-detail cascade behavior, without using org data.

Setup and configuration

Install the package globally with npm on Node.js 20 or newer:

Terminal
npm install -g sf-intelligence

To explore the built-in demo, register the demo command with an MCP host:

Terminal
claude mcp add --scope user sf-intelligence-demo -- npx -y sf-intelligence demo

The demo uses a bundled example org and does not require Salesforce authentication. Running sfi demo directly starts an MCP server and waits for an MCP host; it is not intended as a standalone terminal command.

For an actual Salesforce project, run the initialization command from the project directory and provide the target-org alias:

bash
cd your-sfdx-project
sfi init --target-org my-org-alias

The server can then be registered with an MCP-compatible host such as Claude Desktop, Claude Code, Cursor, VS Code with Copilot, or Codex.

Tools and capabilities

The default MCP surface contains 16 tools. Eight support the main analysis questions, while the remaining tools help the assistant locate components and manage analyses. Named utility tools include:

  • sfi.resolve for matching natural-language references to components
  • sfi.search_components and sfi.get_component for vault lookup
  • sfi.health_check for checking server or vault state
  • sfi.list_analyses and sfi.describe_analysis for discovering available analyses
  • sfi.run_analysis as the gateway for analyses beyond the default set
  • sfi.live_consent for enabling the optional live, read-only plane

Supported analysis areas include field and component usage, deletion-impact verdicts, record-save automation order, unused Apex detection, and effective visibility or edit permissions. The server also supports finding objects and fields, inspecting validation rules, and understanding metadata relationships through plain-language questions.

Limitations and notes

Results describe evidence in the metadata that was retrieved. A statement that no reference was found is not proof that no reference exists outside the checked metadata families. Each relationship is labeled as declared, parsed, or heuristic; heuristic matches should be reviewed before taking action.

The deletion-impact analysis reports what it checked and what it did not check. The read-only design means requests to modify the org are refused rather than executed. The local vault must be refreshed when the Salesforce org changes, and live access remains opt-in. General Salesforce concepts supplied by the deterministic model are structural explanations, not data from the connected org.

Read the full README View source on GitHub

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Frequently Asked Questions about Sf Intelligence

sf-intelligence is an MCP server that connects AI assistants to a local, read-only knowledge base built from one Salesforce org's metadata. Its main tools resolve and search components, retrieve component details, inspect dependencies, run analyses, check vault health, and optionally request capped live read-only access.

Install it with `npm install -g sf-intelligence`, then register the command with your MCP host. The demo can be added with `claude mcp add --scope user sf-intelligence-demo -- npx -y sf-intelligence demo`; an SFDX project can be initialized with `sfi init --target-org my-org-alias`.

Yes, sf-intelligence works with Cursor. The built-in demo can be added through a Cursor MCP install link, and the server can also be registered using the host's MCP configuration without a Salesforce login for demo use.

No, sf-intelligence is read-only and refuses requests to change the org. It retrieves metadata for analysis and can optionally use a live read-only plane after explicit consent.

Yes, questions are answered offline from the local vault after metadata has been refreshed. The optional live connection is disabled by default and is only used when enabled for a read-only request.

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