Skip to main content
Glama
riker-t

Ramp Developer MCP Server

by riker-t

get_endpoint_schema

Retrieve OpenAPI schema for specific API endpoints to understand request parameters, response structures, and required fields for development.

Instructions

🎯 GET PRECISE ENDPOINT SCHEMA - Returns exact OpenAPI schema for specific endpoints.

Perfect for when you need: • Exact request parameter names and types • Response field names and structures
• Required vs optional parameters • Related endpoints for the same use case

Example queries this replaces: • "bills endpoint response schema fields amount vendor status" → Use this tool with /developer/v1/bills • "API pagination limit page_size next cursor" → Get schema for any paginated endpoint • "cards creation request parameters" → Use this tool with /developer/v1/cards

Usage: Provide an endpoint path (and optionally method) to get the complete technical specification.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
endpointYesThe endpoint path (e.g., '/developer/v1/bills', '/developer/v1/limits')
methodNoHTTP method (GET, POST, PUT, etc.). If not specified, will show most relevant method.
include_relatedNoInclude related endpoints for the same use case

Implementation Reference

  • Main execution handler that parses input, finds matching endpoints, handles errors, and formats the schema response as TextContent.
    async def execute(self, arguments: Dict[str, Any]) -> List[TextContent]:
        """Execute schema retrieval"""
        endpoint = arguments.get("endpoint", "").strip()
        method = arguments.get("method", "").upper() if arguments.get("method") else None
        include_related = arguments.get("include_related", True)
        
        if not endpoint:
            return [TextContent(
                type="text",
                text="❌ Please provide an endpoint path (e.g., '/developer/v1/bills')"
            )]
        
        try:
            # Find matching endpoint(s)
            matching_endpoints = self._find_matching_endpoints(endpoint, method)
            
            if not matching_endpoints:
                similar = self._find_similar_endpoints(endpoint)
                suggestion_text = f"\n\n**Similar endpoints available:**\n" + "\n".join([f"• {ep}" for ep in similar[:5]]) if similar else ""
                
                return [TextContent(
                    type="text", 
                    text=f"❌ Endpoint not found: `{method + ' ' if method else ''}{endpoint}`{suggestion_text}"
                )]
            
            # Format the schema response
            result_text = self._format_endpoint_schemas(matching_endpoints, include_related)
            
            return [TextContent(type="text", text=result_text)]
            
        except Exception as e:
            return [TextContent(
                type="text",
                text=f"❌ Error retrieving schema: {str(e)}"
            )]
  • Input schema defining the tool's parameters: endpoint (required string), optional method (enum), and include_related (boolean).
    def input_schema(self) -> Dict[str, Any]:
        return {
            "type": "object",
            "properties": {
                "endpoint": {
                    "type": "string", 
                    "description": "The endpoint path (e.g., '/developer/v1/bills', '/developer/v1/limits')"
                },
                "method": {
                    "type": "string",
                    "description": "HTTP method (GET, POST, PUT, etc.). If not specified, will show most relevant method.",
                    "enum": ["GET", "POST", "PUT", "PATCH", "DELETE"]
                },
                "include_related": {
                    "type": "boolean",
                    "default": True,
                    "description": "Include related endpoints for the same use case"
                }
            },
            "required": ["endpoint"]
        }
  • src/server.py:46-51 (registration)
    Registration of GetEndpointSchemaTool instance in the server's tools list, passed to MCP server handlers.
    tools = [
        PingTool(),
        SearchDocumentationTool(knowledge_base),
        SubmitFeedbackTool(),
        GetEndpointSchemaTool(knowledge_base)
    ]
  • src/server.py:29-29 (registration)
    Import statement bringing GetEndpointSchemaTool into the server module.
    from tools import PingTool, SearchDocumentationTool, SubmitFeedbackTool, GetEndpointSchemaTool
  • Tool name property returning 'get_endpoint_schema', used for registration and invocation.
    def name(self) -> str:
        return "get_endpoint_schema"
Behavior4/5

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

With no annotations provided, the description carries full burden and does well by explaining what the tool returns (exact OpenAPI schema), what inputs are needed (endpoint path, optional method), and behavioral aspects like 'will show most relevant method' when method isn't specified. It doesn't mention rate limits or authentication requirements, but provides solid operational context.

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 well-structured with clear sections (purpose, use cases, examples, usage instructions), front-loaded with the core purpose, and every sentence adds value. No wasted words while maintaining comprehensive coverage.

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

Completeness4/5

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

For a tool with 3 parameters, 100% schema coverage, but no output schema, the description provides excellent context about what the tool returns and when to use it. The main gap is lack of information about return format or structure, which would be helpful since there's no output schema. However, it compensates well with usage examples and scenarios.

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 description coverage is 100%, so the schema already documents all three parameters thoroughly. The description adds some context about 'most relevant method' when method isn't specified, but doesn't provide significant additional parameter semantics beyond what's in the schema. Baseline 3 is appropriate when schema does the heavy lifting.

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 tool's purpose with specific verb ('Returns exact OpenAPI schema') and resource ('for specific endpoints'). It distinguishes itself from sibling tools like 'search_documentation' by focusing on precise technical specifications rather than general documentation search.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool through the 'Perfect for when you need' section listing specific scenarios, and includes 'Example queries this replaces' showing concrete alternatives. It clearly differentiates this tool's use case from general documentation search.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/riker-t/ramp-dev-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server