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RSS3-Network

RSS3 MCP Server

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by RSS3-Network

API-getNodeRewardsByEpoch

Retrieve node reward data for specific epochs from the RSS3 network. Use this tool to query historical reward information for network participants.

Instructions

Retrieve Node rewards by epoch

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • This shared handler executes all dynamically generated API tools, including "API-getNodeRewardsByEpoch". It resolves the tool name to an OpenAPI operation and invokes it using the HttpClient, handling responses and errors appropriately.
    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)
    Registers and lists all available tools dynamically from OpenAPI specs. Tool names are constructed as `{toolName}-{method.name}` truncated to 64 characters, which includes "API-getNodeRewardsByEpoch".
    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 };
    });
  • Special handler within the main tool caller that provides the input schema for any tool, including "API-getNodeRewardsByEpoch", by looking it up from the OpenAPI method definition.
    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`);
    }
  • Helper that converts OpenAPI specifications to MCP tools using OpenAPIToMCPConverter, creating the lookup maps used for tool resolution (openApiLookup). This enables tools like "API-getNodeRewardsByEpoch".
    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) and initializes HttpClients, providing the source for all dynamic tools including "API-getNodeRewardsByEpoch".
    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. It only states the action ('Retrieve') without disclosing behavioral traits such as authentication needs, rate limits, error handling, or the format of returned data. This is inadequate for a tool with zero annotation coverage.

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, clear sentence with no wasted words. It is front-loaded and efficiently conveys the core action, making it highly concise and well-structured.

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 complexity of retrieving rewards data, no annotations, and no output schema, the description is incomplete. It fails to explain what 'Node rewards' includes, the data format, or any prerequisites, leaving significant gaps for an AI agent to understand the tool's behavior.

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, so no parameter information is needed. The description doesn't add parameter details, but this is acceptable given the lack of parameters, aligning with the baseline score for 0 parameters.

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 states the verb ('Retrieve') and resource ('Node rewards by epoch'), which provides a basic purpose. However, it lacks specificity about what 'Node rewards' entail and doesn't differentiate from sibling tools like 'getNodeAssets' or 'getNodeByAddress', 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?

No guidance is provided on when to use this tool versus alternatives. With sibling tools like 'getAllEpochs' and 'getEpochById', there's no indication of whether this tool is for a specific epoch or all epochs, or how it relates to other node-related tools.

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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