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OptimAI Search MCP

OptimAI Search MCP

Official
by OptimaiNetwork
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

optimai_start_search

Start a Web3-focused AI search and get a search ID immediately for async result retrieval. Designed for searches taking 60-90 seconds; fetch the final answer with optimai_get_search.

Instructions

Start a Web3-focused AI search on the OptimAI decentralised network and return immediately with a search ID. Use this because searches commonly take 60-90 seconds or the MCP host has short tool-call timeouts. Call optimai_get_search with the returned ID to check progress and fetch the final answer.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "optimai_start_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesThe search query to start on the OptimAI network

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses that the tool is asynchronous (returns immediately), provides a search ID for polling, and notes the expected search duration. This is strong behavioral context, though it omits details like ID expiration or error conditions.

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?

Two tightly worded sentences. The first states purpose and outcome; the second explains why and what to do next. No filler, all information earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter async tool, the description covers the key behavioral flow: start, get ID, poll with get_search. It lacks an explicit return field name (e.g., 'search_id') and error-handling notes, but these are minor given the tool's low complexity.

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 single parameter 'query' is already well-documented in the schema (100% coverage). The description adds Web3/decentralized context but does not materially expand on the schema's 'The search query to start on the OptimAI network'. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Start'), identifies the resource ('Web3-focused AI search on the OptimAI decentralised network'), and clarifies the immediate outcome ('return immediately with a search ID'). This clearly distinguishes it from sibling tools like optimai_get_search, which fetches results.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool ('searches commonly take 60-90 seconds or the MCP host has short tool-call timeouts') and directs the user to the next step ('Call optimai_get_search with the returned ID'). This provides clear context and a named alternative, satisfying the highest bar.

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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