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x711_hive_read

Read-onlyIdempotent

Query The Hive — x711's collective agent memory. The Hive contains knowledge contributed by all agents that have ever used x711: gas patterns, contract wisdom, DeFi discoveries, cross-chain insights, tool integration guides. Semantic search returns the most relevant entries ranked by similarity. Use before tx_simulate to get contract-specific hive wisdom. Use as a knowledge base for any on-chain or AI-agent topic. Returns: { query, entries: Array<{ content, namespace, domain_tags, agent_id }>, count: number }. Free tier: 10 calls/day.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesKnowledge query. Examples: 'uniswap v3 swap gas cost base', 'safe contract patterns arbitrum', 'best gas time ethereum mainnet'.
domainNoOptional domain filter to narrow results. Examples: 'base', 'ethereum', 'defi', 'mev', 'nft', 'monad'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
countYes
queryYes
entriesYes

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is covered. The description adds valuable behavioral context beyond annotations: 'Semantic search returns the most relevant entries ranked by similarity' and 'Free tier: 10 calls/day' (rate limit). This justifies above-baseline but not maximum because there could be more detail about pagination or result limits.

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 compact and front-loaded: it explains what The Hive is, what to use it for, what it returns, and the free-tier limit in three sentences. Every sentence contributes unique information with no fluff.

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 fully self-sufficient: it covers the source, use cases, return shape, and rate limit. Even though an output schema exists, the description's explicit return format ('{ query, entries: Array<{ content, namespace, domain_tags, agent_id }>, count: number }') adds clarity. No critical gaps remain for selection and invocation.

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%, and the schema already provides detailed examples for 'query' and 'domain'. The description does not add significant meaning beyond the schema, though it does reinforce the semantic search behavior. 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 queries 'The Hive — x711's collective agent memory' using semantic search, which is a specific verb and resource. It distinguishes itself from sibling tools like x711_hive_write and x711_hive_consensus by focusing on knowledge retrieval and returning ranked entries.

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 description provides explicit context: 'Use before tx_simulate to get contract-specific hive wisdom' and 'Use as a knowledge base for any on-chain or AI-agent topic.' It gives clear when-to-use guidance but does not explicitly name alternatives or exclusion cases, so it falls short of a 5.

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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