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RSS3-Network

RSS3 MCP Server

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by RSS3-Network

API-getStakingTransactions

Retrieve staking transaction data from decentralized chains and the RSS3 network to analyze blockchain activity and track staking operations.

Instructions

Retrieve staking transactions

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Generic handler for executing all API tools, including "API-getStakingTransactions". It resolves the tool name to an OpenAPI operation via openApiLookup and calls client.executeOperation with the provided parameters.
    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)
    Dynamically registers all tools, including "API-getStakingTransactions", by converting OpenAPI specs to MCP tools and generating tool names as `${toolName}-${method.name}` truncated to 64 characters.
    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 };
    });
  • Handler for the "API-get-input-schema" tool, which provides the input schema for any API tool like "API-getStakingTransactions" by looking up the corresponding OpenAPI method schema.
    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`);
    }
  • Converts OpenAPI specifications to MCP tools using OpenAPIToMCPConverter, creating the openApiLookup used to resolve and execute tools like "API-getStakingTransactions".
    const mcpToolWithClients = converterWithClients.map((cwc) => {
    	const mcpTools = cwc.converter.convertToMCPTools();
    	return {
    		mcpTools,
    		client: cwc.client,
    	};
    });
  • Fetches OpenAPI specifications from RSS3 Graph Index (gi.rss3.io) and AI (ai.rss3.io) endpoints, initializes HttpClient for each, providing the specs that define endpoints like getStakingTransactions.
    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);
Behavior1/5

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 but only states the action ('retrieve') without details on permissions, rate limits, response format, or whether it returns all transactions or a filtered subset. This is inadequate for a tool that likely involves data retrieval, as it omits critical behavioral traits.

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

Conciseness3/5

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

The description is a single phrase 'Retrieve staking transactions', which is concise but under-specified—it lacks necessary context and structure. While not verbose, it fails to provide a complete picture, making it inefficient in conveying value beyond the tool name.

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 staking transactions and the lack of annotations and output schema, the description is incomplete. It does not explain what data is returned, how transactions are filtered or ordered, or any limitations, leaving significant gaps for an AI agent to understand the tool's full context and usage.

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 does not add parameter details, which is acceptable given the schema's completeness. A baseline score of 4 is appropriate as the description does not need to compensate for any gaps.

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

Purpose2/5

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

The description 'Retrieve staking transactions' restates the tool name 'API-getStakingTransactions' with minimal elaboration, making it tautological. It specifies the verb 'retrieve' and resource 'staking transactions', but lacks differentiation from sibling tools like 'API-getStakingTransactionByHash' or context on what constitutes a staking transaction, leaving the purpose vague beyond the obvious.

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

Usage Guidelines1/5

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 'API-getStakingTransactionByHash' for specific transactions or other activity-related tools in the sibling list. There is no mention of context, prerequisites, or exclusions, leaving the agent without usage direction.

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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