sf-intelligence: offline Salesforce MCP server

Name: sf-intelligence: offline Salesforce MCP server
Author: PranavNagrecha

by PranavNagrechaversion 0.8.0i★ –iRuns on your computer

Registry nameio.github.PranavNagrecha/salesforce-intelligence

Install

Overview

Description from the maker (original English)

Ask your AI what breaks before you change Salesforce. Offline, read-only, cited answers.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Marketing & social media
You need to fill in: SFI_VAULT

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: SFI_VAULT

You need

npmNode.js installed (for the npx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "salesforce-intelligence": {
      "command": "npx",
      "args": [
        "-y",
        "sf-intelligence",
        "mcp"
      ],
      "env": {
        "SFI_VAULT": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add salesforce-intelligence --env SFI_VAULT=your-value -- npx -y sf-intelligence mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "salesforce-intelligence": {
      "command": "npx",
      "args": [
        "-y",
        "sf-intelligence",
        "mcp"
      ],
      "env": {
        "SFI_VAULT": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "salesforce-intelligence": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "sf-intelligence",
        "mcp"
      ],
      "env": {
        "SFI_VAULT": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

SFI_VAULT required env
Absolute path to the org-kb vault this server should answer from, e.g. /org-kb. Set this: without it the server falls back to ./org-kb relative to whatever directory your MCP host happens to launch i…

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account PranavNagrecha, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Marketing & social mediai

More stars than 0% of the other 3565 servers in this category
Published to the registry more recently than 62% of the other 3565 servers in this category
Basic checks equal to or better than 100% of the other 3565 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is sf-intelligence: offline Salesforce MCP server safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: SFI_VAULT

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.8.0) reached the registry 19 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.

What can I use instead?

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

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sf-intelligence

Know what breaks before you change Salesforce. Ask the AI assistant you already use about your org. sf-intelligence runs on your computer, never writes to your org, and every answer names the components it came from.

Website, demo and docs: [sfi.auditforce.cloud](https://sfi.auditforce.cloud) · Works in Claude Code, Claude Desktop, Cursor, VS Code + Copilot and Codex.

What it answers

The questions below were checked by an outside reviewer against a real production org (about 4,700 components) by reading the raw metadata. The answers were right.

Ask	What you get back
Where is this field used?	Every Apex class, flow, layout, validation rule, report and page that references it, each with how it was found (declared, parsed or heuristic)
Can I delete this field?	A verdict (safe, risky, blocking, review) with the reasons and the components behind each one, plus what was not checked
What happens when I save a record?	The save order for one object and one event: triggers, validation rules, flows, approvals, in the sequence they run
Which Apex is dead?	Classes and triggers nothing calls, with the honest tier (definitely, likely, uncertain) and why
Who can see or edit this?	Effective permissions from profile plus permission sets, and why a given user cannot see a record

A real answer from the built-in demo org (a solar installer called Verdant), unedited:

Can I delete `Project__c.Risk_Score__c`? Not safe to delete: blocking. ProjectTriggerHandler writes it and ProjectTriggerHandlerTest reads it (parsed from the Apex); the Residential page layout shows it (declared). Checked: Apex, flows, workflow rules, validation rules, layouts. Not checked: assignment, auto-response and escalation rules, because they were not retrieved.

Try it in three commands

npm install -g sf-intelligence

claude mcp add --scope user sf-intelligence-demo -- npx -y sf-intelligence demo # a built-in demo org, no Salesforce login; then ask your assistant a question cd your-sfdx-project && sfi init --target-org my-org-alias # then: connect your own org (read-only retrieve)

sfi demo on its own starts an MCP server and waits for a host; it is not a command to run in a terminal.

Then register the server in your AI host; the install guide has the exact block for each host. Or one click: Try the built-in demo org in Cursor in one click (no Salesforce login). AI agents installing this server: see llms-install.md.

By default your assistant sees 16 tools: the eight that answer the questions above and eight that help it find its way (sfi.resolve, sfi.search_components, sfi.get_component, sfi.health_check, sfi.list_analyses, sfi.describe_analysis, sfi.run_analysis, sfi.live_consent). Every other analysis sits behind one gateway tool, sfi.run_analysis, and is listed by sfi.list_analyses. The vault is a local folder, answers are served offline, and the optional live read-only plane is off until you turn it on. What your AI assistant does and does not see is in Privacy.

For engineers: how the vault, the dependency graph and your assistant fit together is in How it works; what is deliberately not modeled is in What it does NOT do.

Ask it like a person

You don't need to know the exact API name of anything. A typo-tolerant resolver turns messy phrasing into the right component, and when it isn't sure it asks you a clarifying question instead of guessing:

You: where's the emale field?

It: I found several matches for "email" — which did you mean?

· Email__c on Account

· Email__c on Contact

· Alternate_Email__c on Lead

You: what about the paymnet object?

It: Payment__c — one confident match.

You: is there a "warranty fee" field?

It: Nothing matched that confidently. Your vault was last refreshed on

2026-05-20 — want to pull fresh metadata from the org (/sfi-refresh) in case

it's new, or rephrase?

It never silently commits to a guess. When several components are plausible it asks which you meant; when nothing matches it offers to refresh from the org or stop. A high match score is string similarity, not proof.

How it works

One read-only refresh turns your org into a local vault: the raw metadata, one Markdown page per component, and a dependency graph. From then on every question is answered offline. Your AI assistant resolves the name you used, calls one of the 16 default tools (or sfi.run_analysis for any other analysis), and builds its answer only from what came back, citing each component.

flowchart TB

subgraph REFRESH["Refresh — once, and whenever the org changes"] direction LR ORG[("Salesforce org")] -->|"sf project retrieve (read-only)"| SRC["source/ raw metadata"] SRC -->|"extract + parse Apex / Flow / XML"| VAULT[("Local vault Markdown + DuckDB graph")] end

subgraph ASK["Ask — every question, offline on your machine"] direction TB Q["Your question (plain language)"] --> HOST["Your AI assistant (Claude, Cursor, Copilot, Codex)"] HOST -->|"1 · sfi.resolve"| RESOLVE["The component you meant (or a clarifying question)"] RESOLVE --> HOST HOST -->|"2 · a core tool, or sfi.run_analysis for the rest"| TOOLS["sfi.* analysis"] TOOLS -->|"read"| VAULT TOOLS -.->|"opt-in, read-only, capped"| LIVE[("Live org")] TOOLS -->|"cited components + confidence

what was not checked"| HOST HOST --> ANS["Grounded answer"] end

Every box on the Ask path runs on your machine; the dotted edge to the live org is the only one that can touch Salesforce, and only after you opt in.

How it stays honest

Every answer is built from tool output, never from general Salesforce knowledge. Each relationship carries how it was found: declared (stated in the metadata), parsed (from Apex, Flow or formula source) or heuristic (pattern matching, so spot-check before acting). Each answer says which metadata families were checked and which were not, so "nothing references this" always means "no evidence in what was retrieved". A request to change the org is refused and answered with the read-only check that fits instead. For "what does this imply" questions, a deterministic, offline concept model of general Salesforce truth (never your org's data) adds cited structural claims, such as a master-detail cascade delete or two flows with an undefined order. The details: routing and refusal, architecture and the trust boundary, asking questions, ADR-001 confidence-tagged edges and ADR-008 deterministic reasoning.

What you can ask

Every area answers in plain language; ask "what can you do?" for the live map.

Area	Example questions
Find & identify	"where is the email field?" · "what's the payment object called?"
Understand	"what does this validation rule do?" · "what happens when an Account is saved?" (automation on standard objects works even when the object itself was not retrieved; the answer says so)
Impact & dependencies	"what breaks if I delete this field?" · "is it safe to deactivate this flow?"
Permissions & sharing	"why can't this user see this record?" · "who can edit the Salary field?" · "who holds the Sales Manager permission set?" (live) · "what does user Jane hold?" (live) · "who's in the Support queue?" (live) · "which active users have zero permission-set assignments?" (live)
Automation & code	"what runs on Case create?" · "which Apex methods have no real test coverage?" · "decode this Apex debug log / governor-limit exception to the class that ran"
Decision-support (before you build/change)	"before I add automation to this object, what already runs there?" · "building Apex here — what should I watch out for?" · "before I change/require this field, what breaks?"
Architect & developer	"are there circular Apex dependencies?" · "which classes have no test reference?" · "does the vault still match the live org?"
Integrations	"what external systems does this org talk to?" · "list every outbound endpoint"
Documentation	"give me a tour of this org" · "generate a data dictionary" · "which reports / objects / permission sets have no description?"
Health & audit	"is my vault fresh?" · "where is PII stored?" · "how has the org changed across refreshes?"

All of these are read-only briefings; none of them write to the org. Every component an answer names is backed by a tool call and cited with its canonical id (CustomObject:Account, CustomField:Account.Industry__c), and every relationship with its confidence (declared, parsed or heuristic). The full catalog with the live counts comes from sfi.capabilities (through sfi.run_analysis) or the tools page; the counts are pinned by eval/product-manifest.json, never typed by hand.

What it does NOT do

Boundaries are explicit, and the product tells you plainly when it hits one rather than papering over the gap with general Salesforce knowledge:

Offline by default. Vault tools never call Salesforce mid-conversation. Run /sfi-refresh (or sfi refresh) to update metadata. The opt-in live tools (sfi.live_*) run read-only queries with strict caps and label their answers live_org. They stay off until you grant consent once per org with sfi.live_consent { grant: true } (bound to the org and user, scoped, expiring, stored locally) or set SFI_LIVE_PLANE_ENABLED=1. A per-call liveEnabled: true is intent only, not consent. Hybrid answers combine the vault and the live org and say so. The product never picks which org to query on its own.
No record-level data. The vault stores schema and source, not rows. "How many Opportunities closed last quarter" is a question for your org directly.
Static analysis, not runtime. Dependency edges come from metadat

Only the beginning is shown; read the rest in the repo

sf-intelligence: offline Salesforce MCP server

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Marketing & social mediai

Common questions

Alternatives

MCP YouTube Transcript

Google Analytics MCP

BibiGPT YouTube Video Summarizer

README from the maker

sf-intelligence

What it answers

Try it in three commands

Ask it like a person

How it works

How it stays honest

What you can ask

What it does NOT do