x711

x711_web_search

Read-only

Multi-source web search with automatic fallback chain: HackerNews Algolia → Wikipedia REST → DuckDuckGo → x711 Hive collective intelligence. Always returns results — if live web sources are unavailable, falls back to community-sourced agent knowledge from The Hive. Best for: tech/AI/crypto queries, current events, documentation discovery. Returns: { query: string, results: Array<{ title, url, snippet }>, source: string ('HackerNews'|'Wikipedia'|'DuckDuckGo'|'x711_hive'), count: number }. Free tier: 10 calls/day, no API key needed.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`query`	Yes	Search query. Natural language or keywords. Examples: 'ethereum gas optimization', 'base chain defi protocols', 'monad parallel execution'.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`count`	Yes
`query`	Yes
`source`	Yes
`results`	Yes

TDQS

A4.5/5.0

Behavior5/5

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

The description reveals key behaviors beyond annotations: the automatic fallback chain, guaranteed return of results via community-sourced Hive knowledge, and free-tier rate limit. This adds substantial context about what the agent can expect.

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 concise and front-loaded, with each sentence providing distinct value: purpose, fallback behavior, use cases, output format, and rate limit. No wasted words.

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?

The description is complete given the tool's simplicity: it defines output structure, usage context, fallback behavior, and constraints like the free tier. It substitutes for a full output schema and covers the essentials.

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 coverage for the single 'query' parameter is 100%, as the schema provides examples. The tool description does not add additional parameter-specific semantics beyond restating the query concept. 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 clearly states the tool performs a multi-source web search with a fallback chain, and specifies what it is best for (tech/AI/crypto, current events, documentation). This distinguishes it from sibling tools like x711_deep_search and x711_data_retrieval.

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

Usage Guidelines4/5

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

The 'Best for' provides clear usage context but does not explicitly name alternatives or exclusionary cases. It implies when to use the tool (tech/AI/crypto, current events) but lacks direct contrast with siblings.

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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TDQS

A3.9/5.0

Disambiguation3/5

Many tools have overlapping purposes, such as multiple search tools (web_search, deep_search, data_retrieval) and multiple communication tools (agent_ping, agent_telegram, swarm_broadcast). The descriptions help differentiate, but the boundaries are not always clear.

Naming Consistency4/5

All tools consistently use the 'x711_' prefix and lowercase_with_underscores format. Submodules like agent, hive, and tx follow predictable patterns. Minor deviations (e.g., x711_ask_clerk) are rare and still descriptive.

Tool Count2/5

With 47 tools, the server is excessively large for a typical MCP service. While it aims to be a comprehensive platform, the high count makes navigation and selection cumbersome for an agent.

Completeness4/5

The tool set covers a wide range of agent needs: web access, memory, communication, on-chain transactions, code execution, and more. Minor gaps exist (e.g., no agent deletion tool), but overall it is remarkably complete for the stated purpose of an agent platform.

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