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chainfetch

ChainFETCH MCP Server

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by chainfetch

search_blocks_llm

Search Ethereum blocks using natural language queries. This tool interprets your questions to find relevant blockchain data from over 120 parameters.

Instructions

LLM-powered block search using LLaMA 3.2 3B to select from 120+ parameters

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language query for block search

Implementation Reference

  • index.js:340-353 (registration)
    Registration of the 'search_blocks_llm' tool in the ListTools response, defining its name, description, and input schema.
    {
      name: 'search_blocks_llm',
      description: 'LLM-powered block search using LLaMA 3.2 3B to select from 120+ parameters',
      inputSchema: {
        type: 'object',
        properties: {
          query: {
            type: 'string',
            description: 'Natural language query for block search',
          },
        },
        required: ['query'],
      },
    },
  • Input schema for the 'search_blocks_llm' tool, specifying a required 'query' string parameter.
    inputSchema: {
      type: 'object',
      properties: {
        query: {
          type: 'string',
          description: 'Natural language query for block search',
        },
      },
      required: ['query'],
    },
  • Handler implementation for 'search_blocks_llm' tool within the handleToolCall switch statement. It calls the shared makeRequest method to forward the tool arguments to the ChainFETCH API endpoint '/api/v1/ethereum/blocks/llm_search'.
    case 'search_blocks_llm':
      return await this.makeRequest('/api/v1/ethereum/blocks/llm_search', 'GET', args, null, token);
  • Shared helper method 'makeRequest' used by all tool handlers to make authenticated HTTP requests to the ChainFETCH API, including token handling, query parameter construction, and error propagation.
    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 the LLM model and parameter count (120+), but doesn't disclose important behavioral traits like rate limits, authentication requirements, response format, error conditions, or what 'select from 120+ parameters' actually means in practice. The description is insufficient 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.

Conciseness4/5

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

The description is a single, efficient sentence that conveys the core functionality. It's appropriately sized for a search tool, though it could be slightly more front-loaded by starting with the primary action rather than the implementation detail (LLM-powered).

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?

For a search tool with no annotations and no output schema, the description is incomplete. It doesn't explain what kind of results to expect, how results are formatted, whether there's pagination, or what the '120+ parameters' refers to. The agent lacks sufficient context to understand the tool's full behavior and output.

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% with a single 'query' parameter well-described in the schema. The description adds minimal value beyond the schema by mentioning 'natural language query' is used, but doesn't provide additional context about query formatting, examples, or limitations. Baseline 3 is appropriate when schema does the heavy lifting.

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 'LLM-powered block search' using a specific model (LLaMA 3.2 3B), which is a specific verb+resource combination. It distinguishes from sibling tools by specifying the search method (LLM) and target (blocks), though it doesn't explicitly differentiate from other block search tools like search_blocks_json or search_blocks_semantic.

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 multiple sibling search tools (json, semantic, llm variants for different resources), there's no indication of when LLM-powered search is preferred over other search methods or what specific advantages it offers.

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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