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chainfetch

ChainFETCH MCP Server

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by chainfetch

search_addresses_llm

Find Ethereum addresses using natural language queries. This tool processes your search terms with AI to match against 150+ blockchain parameters.

Instructions

LLM-powered address search using LLaMA 3.2 3B to intelligently select from 150+ parameters

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language query for address search

Implementation Reference

  • index.js:108-121 (registration)
    Registration of the 'search_addresses_llm' tool, including its description and input schema definition.
    {
      name: 'search_addresses_llm',
      description: 'LLM-powered address search using LLaMA 3.2 3B to intelligently select from 150+ parameters',
      inputSchema: {
        type: 'object',
        properties: {
          query: {
            type: 'string',
            description: 'Natural language query for address search',
          },
        },
        required: ['query'],
      },
    },
  • Handler implementation for the 'search_addresses_llm' tool, which forwards the request to the ChainFetch API endpoint '/api/v1/ethereum/addresses/llm_search'.
    case 'search_addresses_llm':
      return await this.makeRequest('/api/v1/ethereum/addresses/llm_search', 'GET', args, null, token);
  • Shared helper function 'makeRequest' that performs authenticated HTTP requests to the ChainFetch API, used by the tool handler.
    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 full burden. It mentions 'LLM-powered' and 'intelligently select from 150+ parameters', which hints at AI-driven behavior, but lacks critical details: it doesn't specify what the 150+ parameters are, how the LLM interprets queries, potential limitations (e.g., accuracy, latency), or output format. For a tool with no annotations, this leaves significant behavioral gaps.

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

Conciseness4/5

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

The description is a single, efficient sentence that front-loads key information: 'LLM-powered address search' and the model used. It avoids redundancy, though it could be slightly more structured by explicitly stating the tool's primary function first (e.g., 'Search addresses using natural language queries via LLaMA 3.2 3B').

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, no output schema, and a single parameter with good schema coverage, the description is incomplete. It lacks details on output (e.g., what data is returned, format), behavioral traits (e.g., how the LLM works, error handling), and clear differentiation from siblings. For an LLM-based tool in a complex sibling set, this leaves too much unspecified.

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% for the single parameter 'query', which is documented as 'Natural language query for address search'. The description adds that it's 'LLM-powered' and uses '150+ parameters', implying the query is processed intelligently, but doesn't provide additional syntax, examples, or constraints beyond what the schema states. With high schema coverage, baseline 3 is appropriate.

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 tool performs 'address search' with 'LLM-powered' capability, specifying it uses 'LLaMA 3.2 3B' to 'intelligently select from 150+ parameters'. This distinguishes it from non-LLM search tools like 'search_addresses_json' and 'search_addresses_semantic', though it doesn't explicitly contrast with those siblings beyond mentioning the LLM approach.

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. It mentions 'LLM-powered' and 'intelligently select from 150+ parameters', which implies it might handle complex or ambiguous queries better than non-LLM tools, but there's no explicit when/when-not advice or named alternatives like 'search_addresses_json' for structured queries.

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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