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ChainFETCH MCP Server

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by chainfetch

search_transactions_semantic

Find Ethereum blockchain transactions using natural language queries with AI-powered semantic search to match transaction content and intent.

Instructions

Semantic search for transactions using AI-powered vector similarity matching

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe query to search for
limitNoThe number of results to return (default: 10)

Implementation Reference

  • Handler implementation for the 'search_transactions_semantic' tool. It proxies the tool call to the ChainFETCH API endpoint '/api/v1/ethereum/transactions/semantic_search' using the shared makeRequest method.
    case 'search_transactions_semantic':
      return await this.makeRequest('/api/v1/ethereum/transactions/semantic_search', 'GET', args, null, token);
  • index.js:152-169 (registration)
    Tool registration in the ListTools response, defining the name, description, and input schema for 'search_transactions_semantic'.
      name: 'search_transactions_semantic',
      description: 'Semantic search for transactions using AI-powered vector similarity matching',
      inputSchema: {
        type: 'object',
        properties: {
          query: {
            type: 'string',
            description: 'The query to search for',
          },
          limit: {
            type: 'integer',
            description: 'The number of results to return (default: 10)',
            default: 10,
          },
        },
        required: ['query'],
      },
    },
  • Input schema definition for the 'search_transactions_semantic' tool, specifying parameters like query (required) and optional limit.
    inputSchema: {
      type: 'object',
      properties: {
        query: {
          type: 'string',
          description: 'The query to search for',
        },
        limit: {
          type: 'integer',
          description: 'The number of results to return (default: 10)',
          default: 10,
        },
      },
      required: ['query'],
    },
  • Shared helper method 'makeRequest' used by all tools, including 'search_transactions_semantic', 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?

With no annotations provided, the description carries full burden but only states the search method without disclosing behavioral traits. It doesn't mention performance characteristics, rate limits, authentication needs, result format, or whether it's read-only (though implied). For a search tool with zero annotation coverage, this is inadequate.

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 that communicates the core purpose without waste. It's appropriately sized and front-loaded, with every word earning its place.

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 for a search tool. It doesn't explain what the results look like, how similarity is measured, or any limitations. With rich sibling tools and complex search functionality, more context is needed.

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 schema already fully documents both parameters (query and limit). The description adds no additional parameter semantics beyond what's in the schema, such as query format examples or limit constraints. Baseline 3 is appropriate when schema does all the work.

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 ('semantic search') and resource ('transactions'), and specifies the method ('AI-powered vector similarity matching'), which distinguishes it from simple keyword search. However, it doesn't explicitly differentiate from sibling tools like search_transactions_json or search_transactions_llm, which would require a 5.

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 like search_transactions_json or search_transactions_llm. It mentions the method but doesn't explain when semantic search is preferable to other search types, leaving the agent with no usage context.

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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