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ChainFETCH MCP Server

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by chainfetch

search_tokens_semantic

Find tokens on Ethereum using AI-powered semantic search that understands natural language queries and matches based on meaning rather than exact text.

Instructions

Semantic search for tokens 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

  • The handler case for 'search_tokens_semantic' tool call, which proxies the arguments to the ChainFetch API semantic search endpoint for tokens.
    case 'search_tokens_semantic':
      return await this.makeRequest('/api/v1/ethereum/tokens/semantic_search', 'GET', args, null, token);
  • Input schema and metadata definition for the 'search_tokens_semantic' tool, provided in the listTools response.
    {
      name: 'search_tokens_semantic',
      description: 'Semantic search for tokens 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'],
      },
  • Shared helper method 'makeRequest' that performs the actual HTTP API call to ChainFetch, used by the search_tokens_semantic 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the search method but doesn't cover critical aspects like whether this is a read-only operation, performance characteristics, rate limits, authentication needs, or what the output looks like. For a search tool with zero annotation coverage, this leaves significant gaps.

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 conveys the essential information without unnecessary words. It's appropriately sized for a search tool and front-loads the key information about what the tool does.

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 the complexity of semantic search and the lack of both annotations and output schema, the description is insufficient. It doesn't explain what 'tokens' refers to in this context, what format results are returned in, how similarity is measured, or any limitations of the semantic search approach. For a tool with no structured behavioral information, the description should provide more context.

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 information beyond what's in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.

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 ('tokens'), and specifies the method ('AI-powered vector similarity matching'), which distinguishes it from non-semantic search tools. However, it doesn't explicitly differentiate from sibling tools like search_tokens_json or search_tokens_llm, which likely use different search methods.

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_tokens_json or search_tokens_llm. It mentions the search method but doesn't explain when semantic search is preferable over other approaches, nor does it mention prerequisites 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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