What can MCP-Salesforce do?

soql query execution with llm-driven schema awareness, sosl full-text search across salesforce objects, salesforce api request formatting and response parsing, mcp server lifecycle management and request handling, object metadata discovery and field schema retrieval, single record retrieval by id with field selection, record creation with field validation and default handling, record update with field-level change tracking, record deletion with id-based targeting, mcp tool registration and schema exposure, mcp tool execution with request routing and error handling, salesforce oauth2 authentication with credential management

MCP-Salesforce

MCP ServerFree

MCP Salesforce connector

Open Source

/ 100

12 capabilities

Capabilities12 decomposed

soql query execution with llm-driven schema awareness

Medium confidence

Executes Salesforce Object Query Language (SOQL) queries through an MCP tool interface, enabling LLMs to construct and run SQL-like queries against Salesforce objects. The connector caches object metadata in the SalesforceClient to reduce API calls and provide schema context to the LLM, allowing the model to understand available fields and relationships before query construction. Queries are validated and executed via the Salesforce REST API, with results returned as structured JSON for LLM processing.

Solves for

I want Claude to query my Salesforce database and return specific records matching criteriaI need to retrieve customer data from Salesforce based on dynamic conditions the LLM determinesI want to enable natural language queries that get translated to SOQL by the LLM

Best for

Teams building AI agents that need read access to Salesforce data

Developers creating LLM-powered reporting and analytics on Salesforce records

Organizations automating data retrieval workflows through conversational interfaces

Requires

Salesforce org with API access enabled

Valid OAuth2 credentials or session token for Salesforce

Python 3.9+

Limitations

SOQL syntax must be valid — LLM-generated queries may require validation/retry loops

Metadata caching is in-memory only — no persistence across server restarts

Query result size limited by Salesforce API response limits (typically 2000 records per query)

What makes it unique

Implements metadata caching within SalesforceClient to provide schema context to LLMs before query execution, reducing the number of schema discovery API calls and enabling more intelligent query construction by the model. The caching layer sits between the MCP tool handler and Salesforce REST API, allowing the LLM to understand object structures without repeated API round-trips.

vs alternatives

Differs from direct Salesforce API clients by exposing SOQL as an MCP tool with built-in schema awareness, enabling LLMs to construct queries with field validation context rather than requiring pre-defined query templates or manual schema documentation.

sosl full-text search across salesforce objects

Medium confidence

Executes Salesforce Object Search Language (SOSL) queries to perform full-text search across multiple Salesforce objects simultaneously. The connector routes SOSL search requests through the MCP tool handler, which formats search parameters and sends them to the Salesforce REST API. Results are returned as structured JSON containing matching records grouped by object type, enabling LLMs to discover records through natural language search terms rather than structured queries.

Solves for

I want to search across all Salesforce objects for records matching a keyword or phraseI need the LLM to find customer records by name, email, or other searchable fieldsI want to enable natural language search that returns results from multiple object types

Best for

Building conversational search interfaces over Salesforce data

Enabling LLM-powered discovery of records across multiple object types

Creating AI assistants that need to find relevant Salesforce records by keyword

Requires

Salesforce org with SOSL search enabled

Valid OAuth2 credentials for Salesforce API access

Python 3.9+

Limitations

SOSL searches are limited to fields marked as searchable in Salesforce

Full-text search may return false positives requiring LLM-based filtering

Search results limited by Salesforce API pagination (typically 2000 results)

What makes it unique

Exposes SOSL as an MCP tool allowing LLMs to perform full-text search across Salesforce objects without requiring knowledge of specific field names or object relationships. The search results are returned in a format optimized for LLM consumption, grouping matches by object type for easier interpretation.

vs alternatives

Provides full-text search capability through MCP, enabling LLMs to discover records organically through keywords rather than requiring structured SOQL queries. This is more flexible than SOQL for exploratory searches but less precise for specific field-based queries.

salesforce api request formatting and response parsing

Medium confidence

Formats HTTP requests to Salesforce REST API endpoints with proper headers, authentication tokens, and request bodies, then parses JSON responses into Python objects. The SalesforceClient handles URL construction, parameter encoding, and error response interpretation. This layer abstracts away HTTP details from the MCP tool handlers, providing a clean interface for Salesforce operations.

Solves for

I want the MCP server to properly format Salesforce API requests with correct headers and authenticationI need to parse Salesforce API responses and extract relevant dataI want to handle Salesforce API errors and convert them to meaningful messages

Best for

Building MCP servers that need to interact with Salesforce REST API

Abstracting HTTP details from tool handlers

Providing consistent error handling across all API calls

Requires

Valid Salesforce API endpoints and authentication tokens

Python 3.9+ with requests library

MCP server running

Limitations

Request formatting is specific to Salesforce REST API — not extensible to other APIs

Response parsing assumes JSON format — no support for other response types

Error handling is basic — Salesforce error codes are passed through without translation

What makes it unique

Encapsulates Salesforce REST API request/response handling in SalesforceClient, providing a clean abstraction layer that tool handlers use without dealing with HTTP details. The client handles authentication header injection, URL construction, and JSON parsing, reducing boilerplate in tool implementations.

vs alternatives

Provides a dedicated API abstraction layer specific to Salesforce, enabling tool handlers to focus on business logic rather than HTTP mechanics. Differs from raw HTTP clients by handling Salesforce-specific conventions like authentication headers and error response formats.

mcp server lifecycle management and request handling

Medium confidence

Implements the MCP Server component that manages the server lifecycle, including initialization, request routing, and shutdown. The server listens for MCP protocol messages from the client, routes them to appropriate handlers (list_tools, call_tool), and sends responses back. The server maintains the SalesforceClient instance and coordinates between the MCP protocol layer and Salesforce API operations.

Solves for

I want to run an MCP server that handles requests from LLM clientsI need the server to properly initialize and manage the Salesforce connectionI want to route MCP protocol messages to the appropriate handlers

Best for

Building MCP servers that integrate with Salesforce

Creating long-running services that handle multiple LLM client requests

Enabling LLM clients to discover and execute Salesforce tools

Requires

Python 3.9+

MCP client configured to connect to the server

Valid Salesforce credentials for SalesforceClient initialization

Limitations

Server is single-threaded — handles one request at a time

No built-in load balancing or horizontal scaling

Server lifecycle is tied to the process — no graceful shutdown handling

What makes it unique

Implements MCP Server as a dedicated component that manages the protocol layer, request routing, and lifecycle. The server maintains a SalesforceClient instance and coordinates between MCP protocol messages and Salesforce API operations, providing a clean separation of concerns.

vs alternatives

Provides a complete MCP server implementation specific to Salesforce, handling protocol details so tool handlers can focus on business logic. Differs from raw MCP implementations by including Salesforce-specific initialization and error handling.

object metadata discovery and field schema retrieval

Medium confidence

Retrieves and caches Salesforce object metadata including field definitions, relationships, and constraints through the SalesforceClient's metadata caching layer. The MCP tool handler exposes a 'get_object_fields' tool that queries the Salesforce Describe API to return field names, types, lengths, and required/updateable flags. Metadata is cached in-memory to reduce API calls when the LLM needs to understand object structures for query construction or validation.

Solves for

I want the LLM to understand what fields are available on a Salesforce object before constructing queriesI need to discover the schema of custom objects in my Salesforce orgI want to validate that a field exists and is updateable before attempting an update operation

Best for

LLM agents that need to dynamically discover Salesforce object schemas

Building adaptive workflows that adjust based on available fields

Enabling LLMs to provide field-level validation before data operations

Requires

Salesforce org with Describe API access

Valid OAuth2 credentials for Salesforce

Python 3.9+

Limitations

Metadata cache is in-memory only — does not persist across server restarts

Cache does not automatically refresh when Salesforce schema changes

Large orgs with many custom objects may experience slower initial metadata loading

What makes it unique

Implements a caching layer in SalesforceClient that stores object metadata in-memory, allowing the LLM to query field definitions without repeated API calls to Salesforce's Describe API. The cache is populated on-demand and reused across multiple tool invocations within a single server session, reducing latency and API quota consumption.

vs alternatives

Provides schema discovery as an MCP tool with built-in caching, enabling LLMs to understand object structures efficiently. Unlike raw Salesforce API clients, the caching layer reduces round-trips and provides metadata in a format optimized for LLM consumption.

single record retrieval by id with field selection

Medium confidence

Fetches individual Salesforce records by their ID through the 'get_record' MCP tool, which calls the Salesforce REST API with optional field filtering. The tool handler accepts a record ID and optional list of fields to retrieve, returning the record as a JSON object. This capability enables LLMs to fetch specific records for inspection, validation, or use in downstream operations without executing full queries.

Solves for

I want to fetch a specific Salesforce record by its ID to inspect its current valuesI need to retrieve only certain fields from a record to reduce payload sizeI want the LLM to validate a record exists before attempting to update it

Best for

LLM agents performing record-specific operations

Building workflows that need to fetch and inspect individual records

Reducing API payload by selecting only required fields

Requires

Valid Salesforce record ID (18-character or 15-character format)

Valid OAuth2 credentials for Salesforce

Python 3.9+

Limitations

Requires knowing the exact record ID — cannot fetch by field values

Field selection must use valid Salesforce field names

No support for fetching related records in a single call

What makes it unique

Provides direct record retrieval by ID as an MCP tool with optional field filtering, allowing LLMs to fetch specific records efficiently without constructing SOQL queries. The tool handler validates the record ID format and field names before making the API call, reducing error rates.

vs alternatives

Simpler and faster than SOQL queries for known record IDs, with built-in field selection to reduce payload. Enables LLMs to fetch records for validation or inspection without query construction overhead.

record creation with field validation and default handling

Medium confidence

Creates new Salesforce records through the 'create_record' MCP tool, which accepts an object type and field values as input. The tool handler sends a POST request to the Salesforce REST API with the provided field data, applying Salesforce's field validation rules and default values. The API returns the newly created record ID and any validation errors, enabling LLMs to create records with automatic constraint enforcement.

Solves for

I want the LLM to create new Salesforce records based on user input or extracted dataI need to ensure new records comply with Salesforce field constraints and required fieldsI want to capture the new record ID for use in subsequent operations

Best for

LLM agents automating record creation workflows

Building conversational interfaces for data entry into Salesforce

Enabling AI-driven data ingestion with automatic validation

Requires

Valid OAuth2 credentials with create permissions on the target object

Knowledge of required fields for the object type

Python 3.9+

Limitations

LLM must provide all required fields — missing required fields cause API errors

Field values must match Salesforce data types and constraints

No support for creating related records in a single call

What makes it unique

Exposes Salesforce record creation as an MCP tool with automatic field validation and constraint enforcement by the Salesforce API. The tool handler formats the request according to Salesforce REST API specifications and returns both success (record ID) and error responses in a format optimized for LLM interpretation.

vs alternatives

Provides record creation through MCP with built-in Salesforce validation, enabling LLMs to create records safely without manual constraint checking. Differs from raw API clients by handling request formatting and error translation for LLM consumption.

record update with field-level change tracking

Medium confidence

Updates existing Salesforce records through the 'update_record' MCP tool, which accepts a record ID and a map of field names to new values. The tool handler sends a PATCH request to the Salesforce REST API, applying only the specified field changes while preserving other field values. Salesforce's field-level validation and update permissions are enforced, and the tool returns success/failure status with any validation errors.

Solves for

I want the LLM to update specific fields on a Salesforce recordI need to modify records based on user input or AI-determined changesI want to ensure only specified fields are changed, leaving others untouched

Best for

LLM agents performing targeted record updates

Building workflows that modify specific fields without full record replacement

Enabling conversational interfaces for record modification

Requires

Valid record ID (15 or 18 characters)

Valid OAuth2 credentials with update permissions on the target object

Knowledge of updateable fields for the object type

Limitations

Field values must match Salesforce data types and constraints

Update permissions are enforced by Salesforce — some fields may be read-only

No support for updating related records in a single call

What makes it unique

Implements record updates via PATCH requests to the Salesforce REST API, allowing LLMs to modify specific fields without affecting others. The tool handler validates field names against cached metadata and enforces Salesforce's field-level update permissions, providing detailed error feedback for failed updates.

vs alternatives

Provides targeted field updates through MCP with automatic validation, enabling LLMs to make precise changes without full record replacement. More efficient than fetching, modifying, and re-saving entire records.

record deletion with id-based targeting

Medium confidence

Deletes Salesforce records through the 'delete_record' MCP tool, which accepts a record ID and sends a DELETE request to the Salesforce REST API. The tool handler validates the record ID format and enforces Salesforce's delete permissions, returning success/failure status. This capability enables LLMs to remove records as part of automated workflows or data cleanup operations.

Solves for

I want the LLM to delete a Salesforce record based on user request or workflow logicI need to remove duplicate or obsolete records from SalesforceI want to enable conversational deletion with confirmation logic

Best for

LLM agents performing record cleanup or deletion workflows

Building conversational interfaces with delete capabilities

Automating data removal based on business logic

Requires

Valid record ID (15 or 18 characters)

Valid OAuth2 credentials with delete permissions on the target object

Python 3.9+

Limitations

Deletion is permanent — no undo capability

Some records may be protected from deletion by Salesforce business rules

No support for cascade deletion of related records

What makes it unique

Exposes record deletion as an MCP tool with built-in permission validation and error handling. The tool handler enforces Salesforce's delete constraints and returns detailed error messages when deletion fails due to business rules or permissions.

vs alternatives

Provides safe record deletion through MCP with automatic permission checking, enabling LLMs to delete records while respecting Salesforce's business rules. Differs from raw API clients by providing error context optimized for LLM interpretation.

mcp tool registration and schema exposure

Medium confidence

Registers all Salesforce operations (SOQL, SOSL, CRUD, metadata) as MCP tools with JSON schema definitions through the 'handle_list_tools' method in the MCP Server component. Each tool includes a schema describing required parameters, parameter types, and descriptions. The MCP server exposes these schemas to the LLM client, enabling the model to discover available operations and understand their signatures without hardcoding tool definitions.

Solves for

I want the LLM to discover what Salesforce operations are availableI need the LLM to understand the parameters and types for each Salesforce toolI want to dynamically expose Salesforce capabilities to the LLM without manual configuration

Best for

Building LLM agents that need to discover available Salesforce operations

Creating flexible MCP servers that expose tools dynamically

Enabling LLMs to understand tool schemas without documentation

Requires

MCP client that supports tool discovery via handle_list_tools

Python 3.9+

MCP server running and connected to the client

Limitations

Tool schemas are static — do not reflect dynamic Salesforce customizations

Schema descriptions are hardcoded in the server — require code changes to update

No support for conditional tool availability based on user permissions

What makes it unique

Implements MCP tool registration through the handle_list_tools method, which returns a complete list of Salesforce operations with JSON schemas. The server defines tool schemas statically in the code, enabling the MCP client to discover and understand all available operations without additional configuration.

vs alternatives

Provides standardized tool discovery through MCP, enabling LLMs to understand available Salesforce operations through a consistent protocol. Differs from custom API clients by using MCP's schema-based tool discovery, making the connector compatible with any MCP-aware LLM client.

mcp tool execution with request routing and error handling

Medium confidence

Routes and executes MCP tool calls through the 'handle_call_tool' method, which receives tool names and arguments from the LLM client and dispatches them to the appropriate handler (SOQL, SOSL, CRUD, metadata). The method validates arguments against tool schemas, executes the operation via SalesforceClient, and returns results or error messages in MCP-compliant format. Error handling includes Salesforce API errors, validation failures, and authentication issues.

Solves for

I want the MCP server to execute Salesforce operations requested by the LLMI need proper error handling and reporting when Salesforce operations failI want to route different tool types to their appropriate handlers

Best for

MCP servers that need to execute tools requested by LLM clients

Building robust error handling for Salesforce API failures

Enabling LLMs to execute operations with automatic error recovery

Requires

MCP client sending tool execution requests

Valid SalesforceClient instance with active authentication

Python 3.9+

Limitations

Error messages from Salesforce API are passed through with minimal translation

No automatic retry logic for transient failures

Tool execution is synchronous — long-running operations may timeout

What makes it unique

Implements tool execution through handle_call_tool, which routes requests to specific handlers based on tool name and validates arguments before execution. The method integrates with SalesforceClient to perform operations and returns results in MCP-compliant format, providing a clean separation between MCP protocol handling and Salesforce API interaction.

vs alternatives

Provides centralized tool execution routing with error handling, enabling MCP clients to execute Salesforce operations reliably. Differs from direct API clients by handling MCP protocol details and providing consistent error reporting across all tool types.

salesforce oauth2 authentication with credential management

Medium confidence

Manages Salesforce authentication through OAuth2 credentials stored in environment variables or configuration files, with the SalesforceClient establishing secure connections using these credentials. The client validates credentials during initialization and maintains an active session for API requests. Authentication errors are caught and reported, enabling the MCP server to fail gracefully if credentials are invalid or expired.

Solves for

I want to securely authenticate the MCP server with Salesforce using OAuth2I need to manage Salesforce credentials without hardcoding them in the serverI want the server to validate authentication before attempting API calls

Best for

Production deployments requiring secure credential management

Teams using environment-based configuration for secrets

Building MCP servers that need to authenticate with Salesforce

Requires

Salesforce OAuth2 credentials (client ID, client secret, refresh token or session token)

Environment variables configured with Salesforce credentials

Python 3.9+

Limitations

Credentials must be provided via environment variables — no interactive login

No support for credential refresh or token rotation

Authentication errors are not automatically recovered

What makes it unique

Implements OAuth2 authentication in SalesforceClient by reading credentials from environment variables and establishing authenticated sessions for all API requests. The client validates credentials during initialization, ensuring the MCP server fails fast if authentication is misconfigured.

vs alternatives

Provides secure credential management through environment variables, avoiding hardcoded secrets in code. Enables the MCP server to authenticate with Salesforce without requiring interactive login or manual token management.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with MCP-Salesforce, ranked by overlap. Discovered automatically through the match graph.

MCP Server34

@salesforce/mcp

MCP Server for interacting with Salesforce instances

salesforce search with sosl and full-text indexingsalesforce soql query execution with mcp transportsalesforce custom object and field querying with dynamic schema bindingsalesforce metadata introspection and schema discovery

4 shared capabilities

MCP Server37

@salesforce/mcp

MCP Server for interacting with Salesforce instances

soql query execution with result streamingsalesforce search (global and object-specific) via mcpsalesforce metadata schema introspection and field discoverysalesforce report and dashboard data retrieval

4 shared capabilities

MCP Server30

@tsmztech/mcp-server-salesforce

A Salesforce connector MCP Server.

salesforce soql query execution with result streamingsalesforce object metadata introspection and schema discoverysalesforce relationship traversal and record linking

3 shared capabilities

Product26

Myko

Instantly answer Salesforce queries with AI-driven real-time...

natural-language-salesforce-queryingconversational-salesforce-analytics

2 shared capabilities

Extension44

Agentforce Vibes

Extension for developing on the Salesforce Platform with the help of generative AI

soql query generation and suggestion

1 shared capability

MCP Server46

Elasticsearch MCP Server

Search, index, and query Elasticsearch clusters via MCP.

elasticsearch query dsl execution with full-text search

1 shared capability

Best For

✓Teams building AI agents that need read access to Salesforce data
✓Developers creating LLM-powered reporting and analytics on Salesforce records
✓Organizations automating data retrieval workflows through conversational interfaces
✓Building conversational search interfaces over Salesforce data
✓Enabling LLM-powered discovery of records across multiple object types
✓Creating AI assistants that need to find relevant Salesforce records by keyword
✓Building MCP servers that need to interact with Salesforce REST API
✓Abstracting HTTP details from tool handlers

Known Limitations

⚠SOQL syntax must be valid — LLM-generated queries may require validation/retry loops
⚠Metadata caching is in-memory only — no persistence across server restarts
⚠Query result size limited by Salesforce API response limits (typically 2000 records per query)
⚠No support for complex joins beyond Salesforce relationship queries
⚠SOSL searches are limited to fields marked as searchable in Salesforce
⚠Full-text search may return false positives requiring LLM-based filtering

Requirements

Salesforce org with API access enabledValid OAuth2 credentials or session token for SalesforcePython 3.9+MCP client configured to communicate with the MCP serverSalesforce org with SOSL search enabledValid OAuth2 credentials for Salesforce API accessMCP client configured to communicate with the serverValid Salesforce API endpoints and authentication tokens

Input / Output

Accepts: SOQL query string (text), Object names and field references (text), Search query string (text), Optional object type filters (text), API endpoint path (text), Request method (GET, POST, PATCH, DELETE), Request parameters and body (structured data), MCP protocol messages from client (JSON), Object name (text, e.g., 'Account', 'Contact'), Record ID (text, 15 or 18 characters), Optional field names array (text), Object type name (text, e.g., 'Account'), Field values as JSON object (structured data), Record ID (text), Field updates as JSON object mapping field names to values (structured data), None — tool listing is initiated by the MCP client, Tool name (text), Tool arguments as JSON object (structured data), OAuth2 credentials from environment variables (text)

Produces: JSON array of record objects, Query execution metadata (record count, execution time), JSON object with results grouped by Salesforce object type, Record metadata including ID, name, and matching fields, Parsed JSON response (structured data), HTTP status codes and error messages, MCP protocol responses (JSON), Tool execution results, JSON object containing field definitions with types, lengths, and constraints, Metadata about relationships and required fields, JSON object representing the Salesforce record, Field values with their types, Newly created record ID (text), Validation errors if creation failed (text), Success/failure status (boolean), Validation errors if update failed (text), Error message if deletion failed (text), JSON array of tool definitions with schemas, Tool names, descriptions, and parameter specifications, Tool execution result (varies by tool type), Error message if execution failed (text), Authenticated session for Salesforce API requests, Authentication error messages if credentials are invalid

UnfragileRank

Adoption14%(30% weight)

Quality43%(25% weight)

Ecosystem52%(25% weight)

Match Graph10%(15% weight)

Freshness75%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: MCP Server

12 capabilities

Visit MCP-Salesforce→

Repository Details

174

Stars

Forks

Python

Language

MIT

License

Topics

mcpmodelcontextprotocolsalesforceserver

Last commit: Apr 13, 2026

About

MCP Salesforce connector

Alternatives to MCP-Salesforce

IntelliCode50Extension

AI-assisted development

Compare →

GitHub Copilot Chat53Extension

AI chat features powered by Copilot

Compare →

GitHub Copilot52Extension

Your AI pair programmer

Compare →

Claude Code for VS Code52Extension

Claude Code for VS Code: Harness the power of Claude Code without leaving your IDE

Compare →

Are you the builder of MCP-Salesforce?

Claim this artifact to get a verified badge, access match analytics, see which intents users search for, and manage your listing.

Claim this artifact →Verification via email

Get the weekly brief

New tools, rising stars, and what's actually worth your time. No spam.

Data Sources

mcp registry

Looking for something else?

Search →

Capabilities12 decomposed

soql query execution with llm-driven schema awareness

Medium confidence

Solves for

Best for

Teams building AI agents that need read access to Salesforce data

Developers creating LLM-powered reporting and analytics on Salesforce records

Organizations automating data retrieval workflows through conversational interfaces

Requires

Salesforce org with API access enabled

Valid OAuth2 credentials or session token for Salesforce

Python 3.9+

Limitations

SOQL syntax must be valid — LLM-generated queries may require validation/retry loops

Metadata caching is in-memory only — no persistence across server restarts

Query result size limited by Salesforce API response limits (typically 2000 records per query)

What makes it unique

vs alternatives

sosl full-text search across salesforce objects

Medium confidence

Solves for

Best for

Building conversational search interfaces over Salesforce data

Enabling LLM-powered discovery of records across multiple object types

Creating AI assistants that need to find relevant Salesforce records by keyword

Requires

Salesforce org with SOSL search enabled

Valid OAuth2 credentials for Salesforce API access

Python 3.9+

Limitations

SOSL searches are limited to fields marked as searchable in Salesforce

Full-text search may return false positives requiring LLM-based filtering

Search results limited by Salesforce API pagination (typically 2000 results)

What makes it unique

vs alternatives

salesforce api request formatting and response parsing

Medium confidence

Solves for

Best for

Building MCP servers that need to interact with Salesforce REST API

Abstracting HTTP details from tool handlers

Providing consistent error handling across all API calls

Requires

Valid Salesforce API endpoints and authentication tokens

Python 3.9+ with requests library

MCP server running

Limitations

Request formatting is specific to Salesforce REST API — not extensible to other APIs

Response parsing assumes JSON format — no support for other response types

Error handling is basic — Salesforce error codes are passed through without translation

What makes it unique

vs alternatives

mcp server lifecycle management and request handling

Medium confidence

Solves for

Best for

Building MCP servers that integrate with Salesforce

Creating long-running services that handle multiple LLM client requests

Enabling LLM clients to discover and execute Salesforce tools

Requires

Python 3.9+

MCP client configured to connect to the server

Valid Salesforce credentials for SalesforceClient initialization

Limitations

Server is single-threaded — handles one request at a time

No built-in load balancing or horizontal scaling

Server lifecycle is tied to the process — no graceful shutdown handling

What makes it unique

vs alternatives

object metadata discovery and field schema retrieval

Medium confidence

Solves for

Best for

LLM agents that need to dynamically discover Salesforce object schemas

Building adaptive workflows that adjust based on available fields

Enabling LLMs to provide field-level validation before data operations

Requires

Salesforce org with Describe API access

Valid OAuth2 credentials for Salesforce

Python 3.9+

Limitations

Metadata cache is in-memory only — does not persist across server restarts

Cache does not automatically refresh when Salesforce schema changes

Large orgs with many custom objects may experience slower initial metadata loading

What makes it unique

vs alternatives

single record retrieval by id with field selection

Medium confidence

Solves for

Best for

LLM agents performing record-specific operations

Building workflows that need to fetch and inspect individual records

Reducing API payload by selecting only required fields

Requires

Valid Salesforce record ID (18-character or 15-character format)

Valid OAuth2 credentials for Salesforce

Python 3.9+

Limitations

Requires knowing the exact record ID — cannot fetch by field values

Field selection must use valid Salesforce field names

No support for fetching related records in a single call

What makes it unique

vs alternatives

record creation with field validation and default handling

Medium confidence

Solves for

Best for

LLM agents automating record creation workflows

Building conversational interfaces for data entry into Salesforce

Enabling AI-driven data ingestion with automatic validation

Requires

Valid OAuth2 credentials with create permissions on the target object

Knowledge of required fields for the object type

Python 3.9+

Limitations

LLM must provide all required fields — missing required fields cause API errors

Field values must match Salesforce data types and constraints

No support for creating related records in a single call

What makes it unique

vs alternatives

record update with field-level change tracking

Medium confidence

Solves for

Best for

LLM agents performing targeted record updates

Building workflows that modify specific fields without full record replacement

Enabling conversational interfaces for record modification

Requires

Valid record ID (15 or 18 characters)

Valid OAuth2 credentials with update permissions on the target object

Knowledge of updateable fields for the object type

Limitations

Field values must match Salesforce data types and constraints

Update permissions are enforced by Salesforce — some fields may be read-only

No support for updating related records in a single call

What makes it unique

vs alternatives

record deletion with id-based targeting

Medium confidence

Solves for

Best for

LLM agents performing record cleanup or deletion workflows

Building conversational interfaces with delete capabilities

Automating data removal based on business logic

Requires

Valid record ID (15 or 18 characters)

Valid OAuth2 credentials with delete permissions on the target object

Python 3.9+

Limitations

Deletion is permanent — no undo capability

Some records may be protected from deletion by Salesforce business rules

No support for cascade deletion of related records

What makes it unique

vs alternatives

mcp tool registration and schema exposure

Medium confidence

Solves for

Best for

Building LLM agents that need to discover available Salesforce operations

Creating flexible MCP servers that expose tools dynamically

Enabling LLMs to understand tool schemas without documentation

Requires

MCP client that supports tool discovery via handle_list_tools

Python 3.9+

MCP server running and connected to the client

Limitations

Tool schemas are static — do not reflect dynamic Salesforce customizations

Schema descriptions are hardcoded in the server — require code changes to update

No support for conditional tool availability based on user permissions

What makes it unique

vs alternatives

mcp tool execution with request routing and error handling

Medium confidence

Solves for

Best for

MCP servers that need to execute tools requested by LLM clients

Building robust error handling for Salesforce API failures

Enabling LLMs to execute operations with automatic error recovery

Requires

MCP client sending tool execution requests

Valid SalesforceClient instance with active authentication

Python 3.9+

Limitations

Error messages from Salesforce API are passed through with minimal translation

No automatic retry logic for transient failures

Tool execution is synchronous — long-running operations may timeout

What makes it unique

vs alternatives

salesforce oauth2 authentication with credential management

Medium confidence

Solves for

Best for

Production deployments requiring secure credential management

Teams using environment-based configuration for secrets

Building MCP servers that need to authenticate with Salesforce

Requires

Salesforce OAuth2 credentials (client ID, client secret, refresh token or session token)

Environment variables configured with Salesforce credentials

Python 3.9+

Limitations

Credentials must be provided via environment variables — no interactive login

No support for credential refresh or token rotation

Authentication errors are not automatically recovered

What makes it unique

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Alternatives to MCP-Salesforce

IntelliCode50Extension

AI-assisted development

Compare →

GitHub Copilot Chat53Extension

AI chat features powered by Copilot

Compare →

GitHub Copilot52Extension

Your AI pair programmer

Compare →

Claude Code for VS Code52Extension

Claude Code for VS Code: Harness the power of Claude Code without leaving your IDE

Compare →

MCP-Salesforce

Capabilities12 decomposed

soql query execution with llm-driven schema awareness

sosl full-text search across salesforce objects

salesforce api request formatting and response parsing

mcp server lifecycle management and request handling

object metadata discovery and field schema retrieval

single record retrieval by id with field selection

record creation with field validation and default handling

record update with field-level change tracking

record deletion with id-based targeting

mcp tool registration and schema exposure

mcp tool execution with request routing and error handling

salesforce oauth2 authentication with credential management

Related Artifactssharing capabilities

@salesforce/mcp

@salesforce/mcp

@tsmztech/mcp-server-salesforce

Myko

Agentforce Vibes

Elasticsearch MCP Server

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

About

Categories

Alternatives to MCP-Salesforce

Are you the builder of MCP-Salesforce?

Get the weekly brief

Data Sources

MCP-Salesforce

Capabilities12 decomposed

soql query execution with llm-driven schema awareness

sosl full-text search across salesforce objects

salesforce api request formatting and response parsing

mcp server lifecycle management and request handling

object metadata discovery and field schema retrieval

single record retrieval by id with field selection

record creation with field validation and default handling

record update with field-level change tracking

record deletion with id-based targeting

mcp tool registration and schema exposure

mcp tool execution with request routing and error handling

salesforce oauth2 authentication with credential management

Related Artifactssharing capabilities

@salesforce/mcp

@salesforce/mcp

@tsmztech/mcp-server-salesforce

Myko

Agentforce Vibes

Elasticsearch MCP Server

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

Repository Details

About

Categories

Alternatives to MCP-Salesforce

Are you the builder of MCP-Salesforce?

Get the weekly brief

Data Sources