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ChainFETCH MCP Server

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by chainfetch

get_address_summary

Generate AI-powered summaries for Ethereum addresses to analyze blockchain data and understand address activity.

Instructions

Get AI-generated summary for a specific address

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
address_hashYesThe address hash to get summary for

Implementation Reference

  • index.js:122-135 (registration)
    Registration of the 'get_address_summary' tool in the ListTools response, including name, description, and input schema definition.
    {
      name: 'get_address_summary',
      description: 'Get AI-generated summary for a specific address',
      inputSchema: {
        type: 'object',
        properties: {
          address_hash: {
            type: 'string',
            description: 'The address hash to get summary for',
          },
        },
        required: ['address_hash'],
      },
    },
  • Handler implementation for 'get_address_summary' tool within the handleToolCall switch statement, which proxies the request to the ChainFETCH API endpoint.
    case 'get_address_summary':
      return await this.makeRequest('/api/v1/ethereum/addresses/summary', 'GET', args, null, token);
  • Shared helper method 'makeRequest' used by the tool handler to make authenticated HTTP requests to the ChainFETCH API.
    async makeRequest(endpoint, method = 'GET', params = {}, body = null, token = null) {
      const chainfetchToken = token || process.env.CHAINFETCH_API_TOKEN;
      
      if (!chainfetchToken) {
        throw new McpError(
          ErrorCode.InvalidRequest,
          'CHAINFETCH_API_TOKEN is required'
        );
      }
    
      const url = new URL(`${API_BASE_URL}${endpoint}`);
      
      // Add query parameters for GET requests
      if (method === 'GET' && Object.keys(params).length > 0) {
        Object.entries(params).forEach(([key, value]) => {
          if (value !== undefined && value !== null) {
            if (Array.isArray(value)) {
              value.forEach(v => url.searchParams.append(`${key}[]`, v));
            } else {
              url.searchParams.append(key, value.toString());
            }
          }
        });
      }
    
      const fetchOptions = {
        method,
        headers: {
          'Authorization': `Bearer ${chainfetchToken}`,
          'Content-Type': 'application/json',
        },
      };
    
      if (body && method !== 'GET') {
        fetchOptions.body = JSON.stringify(body);
      }
    
      const response = await fetch(url.toString(), fetchOptions);
      
      if (!response.ok) {
        const errorText = await response.text();
        throw new McpError(
          ErrorCode.InternalError,
          `API request failed: ${response.status} ${response.statusText} - ${errorText}`
        );
      }
    
      return await response.json();
    }
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 mentions 'AI-generated summary' which hints at potential latency or non-deterministic output, but doesn't disclose behavioral traits like rate limits, authentication needs, or what the summary includes (e.g., format, length). This leaves significant gaps for a tool with no 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, efficient sentence with no wasted words. It's front-loaded with the core action and resource, making it easy to parse quickly.

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 no annotations and no output schema, the description is incomplete. It doesn't explain what the AI-generated summary contains, its format, or any limitations. For a tool that likely returns complex, unstructured data, more context is needed to guide effective use.

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 input schema already documents the 'address_hash' parameter. The description adds no additional meaning beyond implying it's for a 'specific address', which aligns with the schema. Baseline 3 is appropriate as the schema handles parameter documentation adequately.

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

Purpose4/5

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

The description clearly states the verb ('Get') and resource ('AI-generated summary for a specific address'), making the purpose understandable. It distinguishes from siblings like 'get_address_info' by specifying 'summary' rather than 'info', though it doesn't explicitly contrast them.

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 like 'get_address_info' or the various search tools. The description implies usage for a specific address but offers no context about prerequisites, alternatives, or exclusions.

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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