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generateSpecFromCollection

Generates an API specification from a Postman collection. Returns a polling link to track the task status.

Instructions

Generates an API spec for a collection. Returns polling link to task status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
collectionUidYesCollection unique ID
nameYesAPI spec name
elementTypeYesThe 'spec' value
formatYesFormat (openapi, asyncapi, etc.)
typeYesSpecification type

Implementation Reference

  • The GenerateSpecFromCollectionTool class is the handler for the 'generateSpecFromCollection' tool. It defines the tool's schema (input: collectionUid, name, elementType, format, type) and implements run_tool which posts to POST /apis/generate to generate an API spec from a collection.
    class GenerateSpecFromCollectionTool(ToolHandler):
        """Generate spec from collection"""
        
        def __init__(self):
            super().__init__("generateSpecFromCollection")
        
        def get_tool_description(self) -> Tool:
            return Tool(
                name=self.name,
                description="Generates an API spec for a collection. Returns polling link to task status.",
                inputSchema={
                    "type": "object",
                    "properties": {
                        "collectionUid": {
                            "type": "string",
                            "description": "Collection unique ID"
                        },
                        "name": {
                            "type": "string",
                            "description": "API spec name"
                        },
                        "elementType": {
                            "type": "string",
                            "description": "The 'spec' value"
                        },
                        "format": {
                            "type": "string",
                            "description": "Format (openapi, asyncapi, etc.)"
                        },
                        "type": {
                            "type": "string",
                            "description": "Specification type"
                        }
                    },
                    "required": ["collectionUid", "name", "elementType", "format", "type"]
                },
            )
        
        async def run_tool(self, args: dict) -> list[TextContent]:
            body = {
                "collectionUid": args["collectionUid"],
                "name": args["name"],
                "elementType": args["elementType"],
                "format": args["format"],
                "type": args["type"]
            }
            
            result = await postman_api_call("POST", "/apis/generate", body=body)
            return [TextContent(type="text", text=json.dumps(result, indent=2))]
  • The input schema for generateSpecFromCollection defines required properties: collectionUid (string), name (string), elementType (string), format (string), and type (string).
        inputSchema={
            "type": "object",
            "properties": {
                "collectionUid": {
                    "type": "string",
                    "description": "Collection unique ID"
                },
                "name": {
                    "type": "string",
                    "description": "API spec name"
                },
                "elementType": {
                    "type": "string",
                    "description": "The 'spec' value"
                },
                "format": {
                    "type": "string",
                    "description": "Format (openapi, asyncapi, etc.)"
                },
                "type": {
                    "type": "string",
                    "description": "Specification type"
                }
            },
            "required": ["collectionUid", "name", "elementType", "format", "type"]
        },
    )
  • GenerateSpecFromCollectionTool() is instantiated and returned in the register_all_tools() function, registering it as an MCP tool.
    GenerateSpecFromCollectionTool(),
  • The test file confirms 'generateSpecFromCollection' is expected as one of the 41 registered tool names.
    "generateSpecFromCollection",
  • The ToolHandler abstract base class that GenerateSpecFromCollectionTool inherits from. Provides the base __init__ (sets self.name) and defines abstract methods get_tool_description() and run_tool().
    class ToolHandler(ABC):
        """Base class for all Postman tool handlers"""
        
        def __init__(self, name: str):
            self.name = name
        
        @abstractmethod
        def get_tool_description(self) -> Tool:
            """Return the MCP Tool description for this handler"""
            pass
        
        @abstractmethod
        async def run_tool(self, arguments: dict) -> list[TextContent | ImageContent | EmbeddedResource]:
            """Execute the tool with the given arguments"""
            pass
Behavior3/5

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

Mentions 'Returns polling link to task status' which indicates asynchronous behavior, a key trait beyond schema. However, no annotations exist, and the description does not disclose other aspects like permissions or whether it's idempotent.

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?

Two sentences, front-loaded with the core action. No unnecessary words.

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?

The tool is relatively complex (5 required params, no output schema, async with polling link) but the description is minimal. It doesn't explain how to interpret the polling link, the format of the generated spec, or what happens if the collection doesn't exist. Leaves gaps for an agent to infer.

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?

Schema coverage is 100%, so baseline is 3. The description adds no extra meaning beyond the parameter descriptions already in the schema. All parameters are briefly described in schema, and description does not elaborate.

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

Purpose5/5

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

The description clearly states the action ('Generates an API spec') and the resource ('collection'). This distinguishes it from siblings like createSpec (creates spec from scratch) and syncCollectionWithSpec (syncs existing).

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?

No guidance on when to use this vs alternatives like createSpec or syncCollectionWithSpec. Missing context about prerequisites or when to prefer this tool.

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

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