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RSS3-Network

RSS3 MCP Server

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by RSS3-Network

API-getNodeOperationProfitSnapshots

Retrieve snapshots of operation profit data from the RSS3 network to analyze node performance and financial metrics.

Instructions

Retrieve snapshots of operation profit

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Generic handler for executing all API tools, including "API-getNodeOperationProfitSnapshots". It resolves the tool name to an OpenAPI operation via openApiLookup and calls the associated HttpClient's executeOperation method, returning the JSON response or structured error.
    server.setRequestHandler(CallToolRequestSchema, async (request) => {
    	// console.error("call tool", request.params);
    	const { name, arguments: params } = request.params;
    
    	console.error("name", name);
    
    	if (name === "API-get-input-schema") {
    		for (const mcpToolWithClient of mcpToolWithClients) {
    			for (const [toolName, def] of Object.entries(
    				mcpToolWithClient.mcpTools.tools,
    			)) {
    				for (const method of def.methods) {
    					const toolNameWithMethod = `${toolName}-${method.name}`;
    					const truncatedToolName = toolNameWithMethod.slice(0, 64);
    					if (truncatedToolName === params.toolName) {
    						return {
    							content: [
    								{ type: "text", text: JSON.stringify(method.inputSchema) },
    							],
    						};
    					}
    				}
    			}
    		}
    		throw new Error(`Method ${params.toolName} not found`);
    	}
    
    	// find operation
    	const mcpToolWithClient = mcpToolWithClients.find(
    		(t) => t.mcpTools.openApiLookup[name],
    	);
    	if (!mcpToolWithClient) {
    		throw new Error(`Method ${name} not found`);
    	}
    
    	const operation = mcpToolWithClient.mcpTools.openApiLookup[name];
    
    	// execute
    	try {
    		const response = await mcpToolWithClient.client.executeOperation(
    			operation,
    			params,
    		);
    		return {
    			content: [
    				{
    					type: "text", // currently this is the only type that seems to be used by mcp server
    					text: JSON.stringify(response.data), // TODO: pass through the http status code text?
    				},
    			],
    		};
    	} catch (error) {
    		console.error("Error in tool call", error);
    		if (error instanceof HttpClientError) {
    			console.error(
    				"HttpClientError encountered, returning structured error",
    				error,
    			);
    			const data = error.data?.response?.data ?? error.data ?? {};
    			return {
    				content: [
    					{
    						type: "text",
    						text: JSON.stringify({
    							status: "error", // TODO: get this from http status code?
    							...(typeof data === "object" ? data : { data: data }),
    						}),
    					},
    				],
    			};
    		}
    		throw error;
    	}
    });
  • index.js:100-147 (registration)
    Handler for ListToolsRequestSchema that dynamically registers and lists all available tools by constructing names as `${toolName}-${method.name}` (truncated), where tool names like "API-getNodeOperationProfitSnapshots" would be generated from the OpenAPI specs' tool definitions and methods.
    server.setRequestHandler(ListToolsRequestSchema, async () => {
    	console.error("list tools");
    	/**
    	 * @typedef {import("@modelcontextprotocol/sdk/types.js").Tool} Tool
    	 * @type {Tool[]}
    	 */
    	const tools = [];
    
    	for (const mcpToolWithClient of mcpToolWithClients) {
    		for (const [toolName, def] of Object.entries(
    			mcpToolWithClient.mcpTools.tools,
    		)) {
    			for (const method of def.methods) {
    				console.error("method", method);
    				const toolNameWithMethod = `${toolName}-${method.name}`;
    				const truncatedToolName = toolNameWithMethod.slice(0, 64);
    				const trimmedDescription = method.description.split("Error")[0].trim();
    				tools.push({
    					name: truncatedToolName,
    					description: trimmedDescription,
    					inputSchema: {
    						type: "object",
    						properties: {},
    					},
    				});
    			}
    		}
    	}
    
    	tools.unshift({
    		name: "API-get-input-schema",
    		description:
    			"Get the input schema for a given API. We should always use this tool to get the input schema for a given API before calling the API.",
    		inputSchema: {
    			type: "object",
    			properties: {
    				toolName: {
    					type: "string",
    					description: "The name of the tool to get the input schema for",
    				},
    			},
    		},
    	});
    
    	console.error("tools", tools);
    
    	return { tools };
    });
  • Converts fetched OpenAPI specifications into MCP tools using OpenAPIToMCPConverter.convertToMCPTools(), creating the openApiLookup and tools structures used for dynamic tool handling, including for "API-getNodeOperationProfitSnapshots".
    const converterWithClients = openApiSpecs.map((o) => {
    	const converter = new OpenAPIToMCPConverter(o.spec);
    	return {
    		converter,
    		client: o.client,
    	};
    });
    const mcpToolWithClients = converterWithClients.map((cwc) => {
    	const mcpTools = cwc.converter.convertToMCPTools();
    	return {
    		mcpTools,
    		client: cwc.client,
    	};
    });
  • Fetches OpenAPI specifications from RSS3 endpoints (gi.rss3.io and ai.rss3.io), creates HttpClient instances, which define the backend APIs for tools like "API-getNodeOperationProfitSnapshots".
    const openApiSpecs = (
    	await Promise.allSettled([
    		fetch("https://gi.rss3.io/docs/openapi.json").then(async (res) => {
    			if (!res.ok) throw new Error(`HTTP error! status: ${res.status}`);
    			return res.json();
    		}),
    		fetch("https://ai.rss3.io/openapi.json").then(async (res) => {
    			if (!res.ok) throw new Error(`HTTP error! status: ${res.status}`);
    			return res.json();
    		}),
    	]).then((results) => {
    		return results.map((result) => {
    			if (result.status === "fulfilled") {
    				const client = new HttpClient(
    					{
    						baseUrl: result.value.servers[0].url,
    					},
    					result.value,
    				);
    				return {
    					spec: result.value,
    					client,
    				};
    			}
    
    			console.error("Failed to fetch openapi spec", result.reason);
    			return null;
    		});
    	})
    ).filter(Boolean);
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'Retrieve' (implying a read operation) but doesn't mention any behavioral traits such as authentication requirements, rate limits, data format, or whether it returns historical or real-time data. For a tool with zero annotation coverage, this is a significant gap in transparency.

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 no wasted words. It is appropriately sized for a simple tool and front-loads the core action ('Retrieve'), making it easy to understand at a glance.

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 tool's name suggests complexity (involving 'NodeOperationProfitSnapshots'), the lack of annotations, and no output schema, the description is insufficient. It doesn't explain what 'snapshots' entail (e.g., time-series data, aggregated views), the scope of data returned, or how it differs from similar tools, leaving significant gaps in understanding.

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

Parameters4/5

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

The input schema has 0 parameters with 100% coverage, meaning no parameters are documented because none exist. The description doesn't need to add parameter semantics, but it could have mentioned that no inputs are required. Since there are no parameters to explain, a baseline score of 4 is appropriate, as the description doesn't need to compensate for any gaps.

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

Purpose3/5

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

The description 'Retrieve snapshots of operation profit' clearly states the verb ('Retrieve') and resource ('snapshots of operation profit'), providing a basic understanding of the tool's function. However, it lacks specificity about what 'operation profit' refers to in this context and doesn't differentiate from sibling tools like 'getNodeOperationProfitByAddress' or 'getStakerProfitSnapshots', making it somewhat vague.

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. With sibling tools like 'getNodeOperationProfitByAddress' (which appears more specific) and 'getStakerProfitSnapshots' (which seems related), there is no indication of context, prerequisites, or exclusions for this tool's use.

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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