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ChainFETCH MCP Server

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by chainfetch
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JavaScript
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search_smart_contracts_llm

Search Ethereum smart contracts using natural language queries. AI interprets your questions and matches them against 150+ contract parameters for precise results.

Instructions

LLM-powered smart contract search using AI to select from 150+ parameters

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language query for smart contract search

Implementation Reference

  • index.js:765-766 (handler)
    Handler for 'search_smart_contracts_llm' tool: proxies the call to ChainFETCH API endpoint '/api/v1/ethereum/smart-contracts/llm_search' using makeRequest method with GET request and provided arguments.
    case 'search_smart_contracts_llm':
  return await this.makeRequest('/api/v1/ethereum/smart-contracts/llm_search', 'GET', args, null, token);
  • index.js:569-578 (schema)
    Input schema definition for the tool: requires a 'query' string parameter.
    inputSchema: {
  type: 'object',
  properties: {
    query: {
      type: 'string',
      description: 'Natural language query for smart contract search',
    },
  },
  required: ['query'],
},
  • index.js:566-579 (registration)
    Registration of the tool in the tools list provided to MCP server via setRequestHandler for ListToolsRequestSchema.
    {
  name: 'search_smart_contracts_llm',
  description: 'LLM-powered smart contract search using AI to select from 150+ parameters',
  inputSchema: {
    type: 'object',
    properties: {
      query: {
        type: 'string',
        description: 'Natural language query for smart contract search',
      },
    },
    required: ['query'],
  },
},

Tool Definition Quality

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the AI-powered nature and parameter selection capability, but doesn't describe what the search returns, how results are formatted, whether there are rate limits, authentication requirements, or any other operational characteristics. For a search tool with no annotation coverage, 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.

Conciseness5/5

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

The description is extremely concise - a single sentence that efficiently communicates the core functionality without any wasted words. It's front-loaded with the most important information and every element (LLM-powered, smart contract search, AI parameter selection) earns 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 the complexity of an LLM-powered search tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what kind of results to expect, how they're structured, what the '150+ parameters' actually are, or how this differs operationally from other search methods. For a tool in a crowded namespace with many alternatives, 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?

The schema description coverage is 100% with the single 'query' parameter well-documented in the schema. The description adds that this is a 'natural language query' and mentions '150+ parameters' the AI can select from, providing some additional context about the query's nature and the search's capabilities beyond the basic schema documentation.

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's purpose as 'LLM-powered smart contract search using AI to select from 150+ parameters', which includes a specific verb ('search'), resource ('smart contracts'), and method ('LLM-powered'). It distinguishes from non-LLM siblings like 'search_smart_contracts_json' and 'search_smart_contracts_semantic', but doesn't explicitly differentiate from other LLM search tools for different resources.

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 doesn't mention when to choose this LLM-powered search over JSON or semantic search methods for smart contracts, nor does it specify any prerequisites or contextual factors that would make this the preferred choice among the many sibling search tools available.

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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