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AI Gateway MCP Server

by ayatec

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Query live web information with a single AI model to get current answers and sources. Includes query tips for accurate results.

Instructions

Web search with a single model. Use for real-time lookups. For multi-model parallel research, use the research tool.

Query tips (major accuracy improvement):

  • NEVER include specific years (e.g. "2024", "2025") — use "latest", "current", or "newest" instead. The search engine returns recent results automatically; hardcoded years risk missing newer content or retrieving outdated info

  • One topic per query — split broad questions into multiple calls

  • Prefer English for technical topics (official docs are mostly English). Use Japanese when searching for Japan-specific services or local information

  • Include exact terms: version numbers, API names, config keys

  • Yes/No form for fact checks: "Does X support Y?" prevents false assertions

  • Add site: prefix to target official docs

Model guide ($ = cheapest, $$$ = most expensive):

  • perplexity/sonar ($): Extremely cheap (~1/20 cost of others). Fastest. Best for single-fact lookups and GitHub Issue searches

  • openai/gpt-5.4-nano ($$): Latest gen nano with web search, cheaper than gpt-5-mini ($0.20/$1.25 vs $0.25/$2). Search quality comparable to gpt-5-mini for simple lookups, concise output

  • openai/gpt-5-mini ($$): More thorough/comprehensive search results. Best for deep technical docs — migration guides, changelogs, CVE details

  • google/gemini-3-flash ($$ default): General queries, broad coverage, good all-around

  • openai/gpt-5.4-mini ($$$): Strong reasoning + search ($0.75/$4.50). Concise, high-confidence answers. Better cost-performance than claude-haiku-4.5

  • anthropic/claude-haiku-4.5 ($$$): Last resort when other models lack needed info. Most expensive search model ($1/$5) — only use for a different perspective

For parallel multi-model results or diverse perspectives, use the research tool.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelNoSearch-capable model. Default: google/gemini-3-flash ($$ general queries, broad coverage). For quick single-fact lookups: perplexity/sonar ($ extremely cheap, fastest). For cheap search: openai/gpt-5.4-nano ($$ cheaper than gpt-5-mini, latest generation). For official doc precision (migration guides, changelogs): openai/gpt-5-mini ($$). For stronger reasoning in search: openai/gpt-5.4-mini ($$$). Last resort for a different perspective: anthropic/claude-haiku-4.5 ($$$, similar quality to $$ models but significantly more expensive). Also supports other anthropic and openai models with native search.google/gemini-3-flash
queryYesSearch query in natural language with detailed context. Use English for technical topics (most docs are in English), Japanese for Japan-specific info. One topic per query, include specific technical terms (version numbers, API names, config keys) for best accuracy. IMPORTANT: Never include specific years (e.g. "2024", "2025") in queries — use "latest", "current", or "newest" instead. Year numbers become outdated quickly and may miss newer results. See tool description for full query tips and model guide.
max_tokensNoMax output tokens. If set, output is hard-truncated at this limit (may cut off mid-response). Omit to let the model decide output length naturally. Only set when you need strict cost control. Reasoning models consume tokens internally, so set 2x-3x higher than expected visible output
max_retriesNoMax number of retries when search result is poor (empty or unhelpful response). Default: 1. Set to 0 to disable retries.
include_sourcesNoInclude source URLs in the response. When true, appends a Sources section with links at the end
Behavior4/5

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

With no annotations, the description carries the burden. It discloses that the search engine returns recent results automatically and warns about the risks of hardcoded years. It does not mention rate limits or auth, but for a read-only search tool, the disclosure is adequate and adds value beyond schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The description is long but extremely well-structured with clear sections for query tips and model guide. Every sentence delivers actionable guidance, though the overall length makes it slightly verbose. Still, it earns its place due to density.

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

Completeness5/5

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

For a search tool with no output schema, the description covers purpose, when to use vs. the research tool, query optimization strategies, and model selection with cost/performance details. It even notes the Sources section behavior for include_sources, making it comprehensive for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds substantial meaning beyond parameter descriptions: query formulation rules (no specific years, one topic per query, language selection), model cost tiers and use cases, and guidance on max_tokens and include_sources. This goes far beyond the schema's baseline.

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 opens with 'Web search with a single model' and immediately contrasts with the research tool for multi-model use, clearly identifying the tool's scope and distinguishing it from siblings.

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?

It explicitly states when to use this tool ('real-time lookups') and when to use the research tool instead ('multi-model parallel research'). It also provides extensive query construction tips and a nuanced model selection guide with cost/performance trade-offs.

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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