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get_object_fields

Retrieve field names, labels, and types for any Salesforce object to understand its data structure and integration requirements.

Instructions

Retrieves field Names, labels and types for a specific Salesforce object

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
object_nameYesThe name of the Salesforce object (e.g., 'Account', 'Contact')

Implementation Reference

  • MCP tool handler for 'get_object_fields': validates input arguments, invokes the SalesforceClient helper method, and returns the result formatted as MCP TextContent.
    elif name == "get_object_fields":
        object_name = arguments.get("object_name")
        if not object_name:
            raise ValueError("Missing 'object_name' argument")
        if not sf_client.sf:
            raise ValueError("Salesforce connection not established.")
        results = sf_client.get_object_fields(object_name)
        return [
            types.TextContent(
                type="text",
                text=f"{object_name} Metadata (JSON):\n{results}",
            )
        ]
  • Core logic for retrieving and caching Salesforce object fields using describe() API, filtering specific field properties, and returning formatted JSON.
    def get_object_fields(self, object_name: str) -> str:
        """Retrieves field Names, labels and typesfor a specific Salesforce object.
    
        Args:
            object_name (str): The name of the Salesforce object.
    
        Returns:
            str: JSON representation of the object fields.
        """
        if not self.sf:
            raise ValueError("Salesforce connection not established.")
        if object_name not in self.sobjects_cache:
            sf_object = getattr(self.sf, object_name)
            fields = sf_object.describe()['fields']
            filtered_fields = []
            for field in fields:
                filtered_fields.append({
                    'label': field['label'],
                    'name': field['name'],
                    'updateable': field['updateable'],
                    'type': field['type'],
                    'length': field['length'],
                    'picklistValues': field['picklistValues']
                })
            self.sobjects_cache[object_name] = filtered_fields
            
        return json.dumps(self.sobjects_cache[object_name], indent=2)
  • Registers the 'get_object_fields' tool with the MCP server via list_tools(), including description and JSON schema for input validation (requires 'object_name' string).
    types.Tool(
        name="get_object_fields",
        description="Retrieves field Names, labels and types for a specific Salesforce object",
        inputSchema={
            "type": "object",
            "properties": {
                "object_name": {
                    "type": "string",
                    "description": "The name of the Salesforce object (e.g., 'Account', 'Contact')",
                },
            },
            "required": ["object_name"],
        },
    ),

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'Retrieves' implying a read operation, but doesn't cover permissions needed, rate limits, error handling, or response format. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with zero waste, clearly front-loading the purpose. It's appropriately sized for a simple tool with one parameter, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the return values look like (e.g., structure of field data), error conditions, or prerequisites. For a tool that retrieves metadata, more context on output behavior is needed to be fully helpful.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds minimal meaning beyond the input schema, which has 100% coverage and fully documents the single parameter 'object_name'. It implies the tool operates on Salesforce objects but doesn't provide additional syntax or format details. Baseline 3 is appropriate as the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Retrieves') and resource ('field Names, labels and types for a specific Salesforce object'), making the purpose explicit. It doesn't distinguish from siblings like 'get_record' or 'run_soql_query', which might also retrieve object-related data, so it misses full differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives such as 'get_record' (for record data) or 'run_soql_query' (for querying records). It lacks explicit when/when-not instructions or named alternatives, leaving usage context implied at best.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.