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ChainFETCH MCP Server

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by chainfetch

search_smart_contracts_semantic

Find smart contracts by semantic meaning using AI-powered vector similarity matching on Ethereum blockchain data.

Instructions

Semantic search for smart contracts 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

  • Schema definition for the 'search_smart_contracts_semantic' tool, including input schema with query (required) and optional limit.
    {
      name: 'search_smart_contracts_semantic',
      description: 'Semantic search for smart contracts 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'],
      },
    },
  • Handler for 'search_smart_contracts_semantic' tool: proxies the request to the ChainFetch API endpoint '/api/v1/ethereum/smart-contracts/semantic_search' using GET with provided arguments.
    case 'search_smart_contracts_semantic':
      return await this.makeRequest('/api/v1/ethereum/smart-contracts/semantic_search', 'GET', args, null, token);
  • Shared helper method 'makeRequest' used by all tools, including this one, to make authenticated API calls to ChainFetch service.
    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();
    }
  • index.js:612-632 (registration)
    Registration of CallToolRequestSchema handler, which dispatches to handleToolCall based on tool name, enabling execution of 'search_smart_contracts_semantic'.
      this.server.setRequestHandler(CallToolRequestSchema, async (request) => {
        const { name, arguments: args } = request.params;
    
        try {
          const result = await this.handleToolCall(name, args, this.currentToken);
          return {
            content: [
              {
                type: 'text',
                text: JSON.stringify(result, null, 2),
              },
            ],
          };
        } catch (error) {
          throw new McpError(
            ErrorCode.InternalError,
            `Tool execution failed: ${error.message}`
          );
        }
      });
    }
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 offers minimal behavioral disclosure. It mentions 'AI-powered vector similarity matching' which hints at the search methodology, but doesn't describe what the tool returns, performance characteristics, limitations, or any side effects. For a search tool with zero annotation coverage, this is insufficient.

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 zero wasted words. It's appropriately sized and front-loaded with the core functionality.

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 results look like, how semantic matching works in practice, or any limitations. Given the complexity implied by 'AI-powered vector similarity' and lack of structured documentation, 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 fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema (query and limit with default). 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 action ('semantic search') and resource ('smart contracts'), and mentions the method ('AI-powered vector similarity matching'). It distinguishes from non-search siblings but doesn't explicitly differentiate from other smart contract search tools (json, llm variants).

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_smart_contracts_json or search_smart_contracts_llm. It doesn't mention use cases, prerequisites, or when semantic search is preferable to other search methods.

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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