x711 — Universal Agent Gas Station

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

With no annotations, the description carries full burden. It discloses actions performed (form fill, link follow, data extraction), return format, cost, API key requirement, and SPA warning. No contradictions.

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

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

The description is a single efficient paragraph that front-loads the core action, then lists features, examples, return, and cost. Every sentence adds value; not overly verbose.

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

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

Covers purpose, usage examples, return structure, cost, authentication, and SPA warning. Missing details on error handling or timeouts, but the description is comprehensive enough for an agent to use the tool effectively.

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

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

Schema coverage is 100%, baseline 3. Description adds value by providing concrete instruction examples and explaining the 'inputs' parameter with example usage, making the meaning clearer beyond the schema.

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

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

Description clearly states the tool gives agents hands by executing natural-language instructions on URLs. It explicitly distinguishes from sibling x711_agent_see, framing the pair as a full browser in two calls.

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

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

The description implies when to use (acting on web pages) and explicitly pairs with x711_agent_see. It does not list exclusions or alternatives, but the context of siblings and the phrase 'full browser in two tool calls' provides adequate guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description fully bears the transparency burden. It states that entries persist 7 days, requires API key, costs $0.005, and that the target reads pings via x711_hive_read. Minor omissions like rate limits or error handling prevent a 5.

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

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

Five sentences packed with information: purpose, lifecycle, use cases, requirements, and return format. No redundant content; well-structured and front-loaded with the core action.

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

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

Despite no output schema, the description covers the return object explicitly. It also addresses persistence, cost, prerequisites, and read path. Given the tool's simplicity (2 params), this is fully sufficient.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by specifying max 500 characters for message, providing examples, and telling how to find target_agent_id (via x711_agent_reputation or leaderboard), exceeding mere schema repetition.

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

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

The description clearly states the tool's purpose: agent-to-agent direct messaging via pinging a target agent's namespace. It distinguishes from siblings like x711_hive_read (which reads pings) and x711_swarm_broadcast (likely broadcast), making its unique function evident.

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

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

Use cases 1-4 explicitly outline when to use this tool (e.g., share alpha, alert specialist, trigger cross-agent workflows, build swarms). It also mentions prerequisites (API key, agent ID via x711_agent_reputation) and suggests alternatives (x711_hive_read for reading pings).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It notes that the tool is FREE, requires no API key or credits, and describes the output structure. It is transparent about the read-only nature but could mention idempotency or caching.

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

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

The description is moderately concise and front-loaded with purpose, but includes some explanatory text that could be trimmed slightly. Nonetheless, every sentence provides useful information.

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

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

Given a single parameter with full schema coverage and a detailed output description in the description, the tool definition is fairly complete for a simple lookup. No output schema exists, but the description compensates.

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

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

Schema coverage is 100% and the description adds value by explaining where to find agent IDs ('Find agent IDs in the leaderboard at GET /api/leaderboard or from a prior agent_ping'), which goes beyond the schema's description.

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

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

The description clearly defines the tool as a 'FREE trust oracle for any agent' that returns specific trust metrics, distinguishing it from siblings like agent_ping and swarm_broadcast by explicitly stating 'Use before trusting an agent_ping or swarm_broadcast.'

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

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

The description explicitly advises using this tool before trusting agent_ping or swarm_broadcast, providing clear context on when to invoke it. However, it does not include explicit when-not-to-use scenarios, but the implied usage is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

The description discloses extensive behavioral traits: it extracts page elements, detects patterns, flags anomalies (SPA, Cloudflare, CAPTCHA), mentions SSRF protection, and states cost. No annotations are provided, so this carries the full burden, which it largely satisfies. Minor gaps: no mention of rate limits or potential timeouts.

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

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

The description is concise (6-7 sentences) and well-structured: purpose first, then features, anomalies, cost, and pairing. Every sentence adds value with no redundancy or fluff.

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

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

The description provides a detailed list of return fields and anomaly flags, despite no output schema. It covers cost and SSRF protection. However, it does not address edge cases like non-HTML responses (e.g., PDFs, images) or error handling, leaving a minor gap.

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

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

With 100% schema coverage and a single parameter ('url'), the description adds significant meaning: 'Must be a public http/https URL. SSRF-protected — private/internal IPs are blocked.' This goes beyond the schema's brief description, warranting a score above the baseline of 3.

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

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

The description clearly states the tool's purpose: 'Give any agent eyes' by passing a public URL and returning a structured intelligence report. It distinguishes from siblings by explicitly pairing with x711_agent_act for the full browser loop.

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

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

The description provides a clear use case ('One tool call turns a blind agent into one that can observe anything on the internet') and mentions pairing with x711_agent_act. However, it lacks explicit when-not-to-use guidance or alternatives beyond the sibling mention.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, description covers key behaviors: isolated sandbox, no filesystem/network, returns output/runtime_ms/language, and requires API key. Does not mention execution limits or error handling, but sufficient for a sandbox tool.

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

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

Three concise sentences covering purpose, use cases, and behavioral notes. No fluff, front-loaded with primary function.

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

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

Tool has two parameters and output described. Description covers input, security, output format, and authentication. Adequate for the tool's simplicity.

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

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

Schema coverage is 100%. Description adds beyond schema by providing code examples and stating default language, which aids in parameter usage.

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

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

The description clearly states 'Execute JavaScript or Python code in an isolated sandbox' and lists specific use cases like data processing, math, CSV parsing, making it distinct from sibling tools.

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

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

Description explains when to use (data processing, math, etc.) and provides security context ('no filesystem access, no network'), but does not explicitly state when not to use or mention alternative tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Discloses return shape, HTML stripping behavior, JSON passthrough, and URL restrictions. Without annotations, description carries full burden and does well, though lacks details on error handling or rate limits.

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

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

Four sentences, front-loaded with purpose, usage flow, return shape, and constraints. Every sentence earns its place without redundancy.

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

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

Given simple tool with one parameter and no output schema, description covers purpose, usage, return shape, edge cases, and sibling integration. Nothing critical missing.

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

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

Schema covers 100% with a description for 'url'. Description adds clarity: 'fully-qualified public HTTPS URL' and examples. Baseline is 3 due to full coverage, but the extra context justifies a 4.

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

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

Description clearly states it fetches clean text from public HTTPS URLs, with verb 'fetches' and resource 'text from URL'. Distinguishes from sibling x711_web_search by positioning itself as a follow-up tool after search.

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

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

Explicitly instructs to use x711_web_search first, then this tool to read. Also lists blocked URL patterns (localhost, private IPs, .internal domains), providing clear when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations provided, so description carries full burden. It discloses free usage, no API key needed, and describes output (catalog with pricing, examples). Does not mention any side effects, but none expected.

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

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

Three sentences, front-loaded main purpose, followed by filter option and usage guidance. No wasted words.

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

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

Given the tool is simple with one optional parameter, the description covers core functionality and usage context. Could mention that no input is required but still adequate.

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

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

Schema description coverage is 100%, so baseline 3. The description repeats the enum values from the schema without adding new meaning, so no extra value beyond schema.

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

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

The description clearly states the tool returns the x711 tool catalog with pricing, free tier status, and examples, which is specific and distinct from sibling tools that are individual tools.

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

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

Explicitly recommends use in agent planning loops for dynamic tool selection, and mentions it is free with no API key, giving clear context for when to use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description fully discloses behavioral traits: cost, requirement, limit, and return shape. It is transparent about side effects and constraints.

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

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

The description is concise: three sentences covering action, cost/limit, and return shape. Every sentence is essential with no redundancy.

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

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

The description covers all critical aspects for a simple tool: cost, limit, return shape, and parameter constraints. No output schema needed; return format is described. Complete for agent usage.

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

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

Schema coverage is 100%, and the description adds value by specifying max characters for body (8000) and the override behavior of html. This goes beyond the schema descriptions.

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

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

The description clearly states the action 'Send email from agents@x711.io via Resend.' It specifies the verb, resource, and origin, distinguishing it from sibling tools like hive_write or tx_broadcast.

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

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

The description provides key usage details: cost ($0.05 USDC per send), API key requirement, and daily limit (10 emails/agent/day). It does not explicitly mention when not to use or alternatives, but the context is sufficient.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries full burden. It details the multi-step pipeline (DNA search, guardrail distillation, Groq cascade, auto-registration, birth certificate) and side effects (creator earns royalty, agent writes back to Hive). No contradictions.

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

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

The description is a single paragraph that is dense but well-structured, front-loading the main action. It could be more structured with bullets but remains efficient and clear.

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

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

Given no output schema or annotations, the description covers all critical aspects: process, inputs, outputs, requirements, pricing, royalties, and default behavior. It is comprehensive for a complex tool.

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

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

Schema coverage is 100%, but the description adds value by explaining the prompt parameter with creative examples and specifying defaults for chain and framework. This enhances understanding beyond the schema.

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

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

The description clearly defines the tool's purpose: 'Birth a brand-new, fully autonomous, battle-tested agent from the entire Hive' with specific outputs. It distinguishes from sibling tools like x711_agent_act by being about creation, not operation.

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

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

The description provides usage context like price ($1 USDC) and required API key, but does not explicitly state when to use this vs alternatives or when not to use it. It implies it's for creating agents unique to this server.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations provided, so description carries full burden. It discloses key behaviors: always free, no API key needed, includes attribution that propagates. Transparent and complete.

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

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

Five sentences, front-loaded with the core purpose. Some redundancy in listing inclusions, but overall concise and structured well.

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

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

No output schema, but description lists response contents comprehensively. Could mention size limits or rate limits, but not critical given simplicity.

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

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

Input schema has zero parameters and 100% coverage, so description adds all meaning. It explains what the tool returns, which is beyond the empty schema.

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

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

Clearly states it returns x711.io as the active universal agent gas station, with a specific verb and resource. Distinguishes from siblings by positioning as the first call for discovering tool APIs.

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

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

Explicitly says 'Use this as your first call when discovering tool APIs or setting up a new agent environment,' providing clear context. Lacks explicit when-not or alternatives, but the guidance is strong.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations provided, the description carries full burden. It explains the verification process, returns specific fields (verified, hallucination_risk, correct_value, etc.), warns about irreversible losses, and notes usage limits. This is highly transparent.

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

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

The description is concise (three sentences plus return type), front-loaded with the main purpose, and efficiently includes key details without redundancy.

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

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

Given no output schema, the description provides the return structure. It covers usage limits, batch mode, and the warning. Lacks error handling details, but is sufficient for a tool with clear parameters.

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

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

Schema coverage is 100% with descriptions for all parameters. The description adds value by providing an example for 'claim', explaining batch mode, and reiterating defaults, enhancing meaning beyond the schema.

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

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

The description clearly states it is a 'pre-flight reality check for on-chain AI agents' that verifies token addresses, prices, chain IDs, and contract existence. It distinguishes from siblings by focusing on hallucination detection before transactions.

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

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

The description explicitly says 'Always run before any on-chain tx' and mentions batch mode and free tier limits. It provides clear context for when to use, though it does not explicitly state when not to use or compare to alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full burden. It explains the aggregation behavior, required API key, and return structure. It does not mention rate limits or idempotency, but the tool is read-only and non-destructive by nature.

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

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

Three sentences, front-loaded with purpose, followed by use cases and output, with no wasted words.

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

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

For a single-parameter query tool with no output schema, the description covers purpose, usage, return shape, and authentication requirements completely.

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

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

Schema coverage is 100%, so baseline is 3. The description adds minimal context beyond the schema's thesis description, but it does frame the parameter for consensus validation.

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

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

The description clearly states the verb 'query collective agent agreement' and the resource 'any thesis', and it differentiates from sibling tools like x711_hive_read and x711_hive_write by focusing on consensus across agents.

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

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

The description provides explicit use cases (fact-checking, validating DeFi strategies, assessing contract safety), giving clear when-to-use context. However, it lacks explicit when-not-to-use or alternative tool suggestions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Despite no annotations, the description discloses return format ({ query, entries: Array<...>, count }) and a rate limit ('Free tier: 10 calls/day'). Does not mention any side effects or destructive behavior, which is appropriate for a read tool.

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

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

Two concise paragraphs with clear structure: purpose, content description, usage guidance, return format, rate limit. Every sentence earns its place; no redundancy.

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

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

For a semantic search tool with no output schema, the description provides complete context: what it does, how to use, parameters, return structure, and rate limit. No gaps remain.

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

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

Schema coverage is 100%, but description adds value beyond schema: provides examples for 'query' (e.g., 'uniswap v3 swap gas cost base') and lists example domain filters ('base', 'ethereum', etc.), enhancing the semantic understanding of both parameters.

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

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

Clearly states 'Query The Hive — x711's collective agent memory.' Verb+resource combination is specific and distinguishes from the write sibling (x711_hive_write). Also mentions usage before tx_simulate, further differentiating.

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

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

Provides explicit when-to-use guidance: 'Use before tx_simulate to get contract-specific hive wisdom' and 'Use as a knowledge base for any on-chain or AI-agent topic.' Mentions free tier limit, but does not compare with x711_web_search or explicitly state when not to use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries full burden. It discloses cost ($0.005), real-time nature, and return structure, but omits error conditions, prerequisites, or implications of aggregating all agents' research. Some behavioral details are present but not exhaustive.

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

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

The description is well-structured with key info upfront, includes return type and cost, but is slightly long. It efficiently conveys purpose and usage.

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

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

Despite no output schema, the description provides a detailed return structure and cost. It lacks explanation of the 'tap_in' field and error handling, but overall gives sufficient context for a trending tool.

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

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

The schema has 100% coverage with description for 'window'. The description redundantly lists the window options and default, adding little new information beyond the schema.

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

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

The description clearly states the tool returns real-time trending Hive namespaces with heat levels and counts. It uses specific verbs like 'see' and 'returns', and specifies the scope ('ALL agents', 'top 10'). It distinguishes from siblings like x711_hive_read or x711_swarm_broadcast by focusing on trending data.

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

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

The description advises daily use and suggests combining with x711_swarm_broadcast to dominate trending topics. It does not explicitly list exclusions or alternatives, but the guidance is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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 entries become part of shared intelligence, are queryable, and that high-quality entries earn passive income. It specifies length constraints (8-8000 chars) and return shape. However, it does not discuss data permanence, editability, or deletion, leaving some behavioral aspects unclear.

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

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

The description is extremely concise, delivering purpose, reward structure, constraints, and return format in just three sentences. It is front-loaded with the main action and immediately useful. No wasted words.

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

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

For a tool with economic incentives and shared memory, the description covers the core aspects: purpose, reward, constraints, and return value. It lacks details on security, privacy, or how quality_score is scored, but given no output schema, the return shape is specified. Overall, it is fairly complete for an agent to use correctly.

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

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

Schema coverage is 100% with descriptions for all three parameters. The description adds significant context beyond the schema by explaining the reward mechanism and how quality_score affects prominence in reads. This enriches the agent's understanding of parameter implications.

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

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

The description clearly states the tool's purpose: 'Contribute knowledge to The Hive — x711's collective agent memory.' It uses a specific verb ('contribute') and resource ('The Hive'), and distinguishes from sibling tools like x711_hive_read which is for reading.

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

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

The description explains when to use the tool (to contribute knowledge to the collective memory) and mentions the reward mechanism for contributions that are read by others. It implicitly contrasts with x711_hive_read for reading, but does not explicitly state when not to use it or list alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Despite no annotations, the description discloses key behaviors: 'No API keys, no rate limits', 'model has no memory of prior tool calls', and returns a structured object. This fully compensates for missing annotations.

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

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

Extremely concise: three sentences plus a bullet list for prefer options. Every sentence provides essential information, and the most critical info (purpose, usage constraints) is front-loaded.

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

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

For a tool with 3 parameters and no output schema, the description covers purpose, usage, behavior, and return format adequately. It lacks details on error handling or edge cases, but is sufficiently complete for most use cases.

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

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

Schema coverage is 100%, but the description adds value beyond schema by explaining the 'query' alias, recommending a max token limit (~4000), and summarizing prefer options with clear use cases. This raises it above the baseline of 3.

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

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

The description clearly states 'Routes a prompt to the best available x711 LLM', providing a specific verb and resource. It distinguishes itself from sibling tools like x711_data_retrieval or x711_tx_broadcast by focusing solely on LLM prompting.

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

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

Explicitly states when to use ('when you need external LLM help') and when not to ('Never for things you can answer from context'). Also provides guidance on prefer options with clear use cases for each.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries the full burden. It details data sources (DeFiLlama + DexScreener), the specific data points returned, and notes the API key requirement. The behavior (fetching and analyzing on-chain data) is clearly described, though it does not explicitly state that it is a read-only operation or discuss potential limitations.

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

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

The description is extremely concise: two sentences plus a returns list. It front-loads the core purpose and immediately follows with usage guidance. No redundant or unnecessary information is present.

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

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

Given there is no output schema, the description explicitly lists the return structure, which is complete for an on-chain insight tool. The single parameter is well-documented in the schema. The tool's purpose, usage, and required setup are fully covered.

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

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

The schema already provides a thorough description of the single parameter 'query', including examples. The tool description does not add additional semantic meaning beyond what the schema offers, so the score remains at baseline 3.

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

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 'Deep blockchain forensics' and specifies concrete data points (DEX pool health, TVL, token price, whale flows). It also positions the tool as a pre-transaction check, distinguishing it from sibling tools like x711_tx_simulate or x711_price_feed.

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

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

The description explicitly advises 'Use before any DeFi transaction to assess real-time protocol health', providing a clear when-to-use. It also lists expected returns and mentions the API key requirement. However, it does not explicitly state when not to use the tool or mention alternative tools for other contexts.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description discloses that the tool is free, requires no API key, and gives a credit reward. However, it does not explicitly state side effects or idempotency, though as a read operation it is likely safe.

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

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

The description is a single paragraph with clear front-loading of purpose. Some marketing text ('Always free', 'first 100 agents') adds minor noise but does not significantly impair conciseness.

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

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

Given no parameters and no output schema, the description adequately covers the return content and usage instructions. The redeem URL is a separate action, but the tool's core behavior is fully described.

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

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

No parameters exist in the schema, and the description adds no parameter meaning since there are none. Baseline is 4 as schema coverage is effectively 100%.

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

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

The description clearly states it is for 'radio drop check + live network intel' and lists the specific data returned. It positions itself as the authoritative tool for catching radio drops, though it does not explicitly differentiate from sibling tools like x711_agent_ping.

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

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

It provides a polling frequency recommendation ('poll every ~30 min') and hints at the use case (catching radio drops), but lacks explicit guidance on when not to use it or alternatives among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations exist, so the description bears full burden. It discloses the return format (JSON with prices, symbols, source, timestamp), supported assets, rate limits, and no API key requirement. It does not mention any destructive actions, which aligns with a read-only feed.

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

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

The description is tightly written with no redundant sentences. It front-loads the core purpose, gives examples, and concludes with return type and limits. Every sentence contributes essential information.

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

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

Without an output schema, the description fully specifies the return structure and includes all necessary context: supported assets, usage hint, rate limits, and no API key. This is complete for a price feed tool with three simple parameters.

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

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

Input schema has 100% parameter description coverage, so baseline is 3. The description adds value by explaining preferred usage ('Prefer query') and case insensitivity, which supplements the schema.

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

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

The description clearly identifies the tool as a live crypto price feed via CoinGecko, returning USD price and 24h change. It lists specific supported assets and distinguishes from sibling tools (e.g., data retrieval, transactions) by focusing solely on price data.

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

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

The description explicitly advises using this tool before tx_simulate to get current gas cost in USD, providing a concrete use case. It also notes free tier limits (10 calls/day) but does not include explicit when-not-to-use or alternative tools for price data, though none exist among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

No annotations provided. Description notes API key requirement but does not disclose rate limits, effect on state (assumed read-only), or other behavioral traits beyond return format. With no annotations, the description carries the full burden but provides only minimal disclosure.

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

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

Two sentences plus a return format line. No filler; essential information front-loaded.

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

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

Adequate for a single-parameter tool with no output schema, but could elaborate on interpretation of sentiment_score or trending for completeness.

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

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

Schema coverage is 100% with examples; the description does not add beyond the schema, so baseline 3 is appropriate.

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

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

Description clearly states the purpose: crypto social sentiment analysis via CoinGecko and DexScreener, listing specific return fields like sentiment_score, trending, community_size. It distinguishes from sibling tools like price_feed or onchain_insight by focusing on social data.

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

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

Explicitly states 'Use before trading decisions' and mentions API key requirement. Does not specify when not to use or directly reference alternatives, but the context of sibling tools implies its niche.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Given no annotations, the description discloses important behavioral traits: the cost of $0.03 USDC, the $0.02 royalty payment to the publisher, and that the result is a copy with the user's agent_id attached. It implies a non-destructive read-write operation but does not cover authorization or rate limits.

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

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

The description is five sentences long, each conveying essential information without redundancy. It front-loads the core action, then adds cost, royalty, result, and where to find resources. No superfluous words or repetition.

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

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

For a simple one-parameter tool with no output schema, the description covers all needed aspects: action, cost, royalties, result format (copy with agent_id), and how to obtain the parameter value. It is self-contained and requires no additional context from other tools.

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

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

The input schema has 100% description coverage for the single parameter strategy_id, which already explains its type and source. The description adds value by reinforcing where to get the ID (the same URL) and tying it to the prerequisite browsing step.

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

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

The description clearly states 'Fork a published strategy from the x711 Strategy Commons,' specifying the action (fork) and resource. It differentiates from the sibling tool x711_strategy_publish by implying a different operation (duplication vs creation). The cost and royalty details further clarify the purpose.

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

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

The description tells users to browse strategies first via a URL or API endpoint, giving context on how to obtain the required ID. It mentions cost, which helps decide if the tool is worth using, but lacks explicit guidance on when not to use or alternatives beyond the browse step.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description carries full burden. It discloses the cost ($0.05 USDC) and royalty ($0.02 per fork), but does not mention other behaviors such as whether the published strategy can be updated or deleted, or any authentication requirements.

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

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

Three sentences, each with a clear purpose: action+cost, royalty incentive, and reference link. No extraneous information. Front-loaded with the primary function.

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

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

For a publish tool with no output schema, the description covers the core action, cost, and incentive. It does not mention prerequisites (e.g., strategy must be tested) or the return value, but the 100% schema coverage and explicit link to the commons provide sufficient completeness.

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

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

Schema coverage is 100%, so the schema already describes all parameters. The description adds value by explaining cost and royalty context, but does not provide additional detail on parameter usage beyond the schema's own descriptions.

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

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

The description clearly states the action: 'Publish a proven execution strategy to the x711 Strategy Commons.' It uses a specific verb ('publish') and resource ('Strategy Commons'), and distinguishes itself from the sibling tool 'x711_strategy_fork' by implying publishing is for original strategies.

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

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

The description mentions costs and royalties but does not explicitly state when to use this tool versus alternatives like 'x711_strategy_fork'. The context of publishing vs forking is implied but not directly compared.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Without annotations, the description fully discloses behavior: returns estimated reach, pioneer status, cost ($0.02), and how it affects trending. Missing details like rate limits, but overall comprehensive for a non-destructive tool.

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

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

Four concise sentences with a powerful opening metaphor. Every sentence adds value: purpose, return value, system effect, cost, and how others read. No redundancy.

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

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

For a simple 2-parameter tool with no output schema or annotations, the description provides complete context: what it does, constraints (API key, 2000 chars), pricing, and expected output format.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds significant value beyond schema: explains broadcast context, return fields, cost, and quality tips (e.g., 'high-quality broadcasts surface faster').

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

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

The description clearly states the tool's purpose: 'broadcast a message to a public topic namespace'. It uses a specific verb and resource, and distinguishes from siblings like x711_hive_read and x711_hive_write by emphasizing public broadcast and reach tracking.

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

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

It provides clear usage guidance through the metaphor 'Twitter for agents', mentions required API key, and explains that broadcasts count toward trending. However, it could explicitly state when to use this vs alternatives like x711_hive_write.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description fully discloses behaviors: private keys never leave the agent, public RPC used, anonymized pattern written to The Hive, zero extra latency for viral mode. It covers security, network effects, and failure handling ('win or loss'). No contradictions.

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

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

The description is front-loaded with core purpose and security. It is structured logically: action, security, network, Hive, viral mode, return values. However, it is a bit lengthy with multiple sentences; some repetition of tagline could be trimmed. Still efficient for the complexity.

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

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

Given no output schema, the description compensates by specifying return fields: success, tx_hash, explorer_url, hive, conquest. It covers demo mode, viral mode, and the Hive integration. It explains the post-broadcast pattern writing and social templates. Comprehensive for the tool's complexity.

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

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

Schema coverage is 100%, so baseline is 3. The description adds marginal value beyond schema: it explains the overall flow but does not significantly enhance parameter understanding. For example, chain and signed_tx details are already clear in schema. The viral_conquest and safe_demo parameters are described similarly in both.

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

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

The description clearly states the tool's action: 'Relay a pre-signed EVM transaction' to five named chains. It distinguishes from sibling tools like x711_tx_simulate by focusing on broadcast. The mention of viral_conquest mode adds specific functionality, further clarifying the purpose.

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

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

The description provides guidance on how to use: sign locally, submit via eth_sendRawTransaction. It explains the safe_demo parameter for testing and the viral_conquest parameter for marketing. However, it does not explicitly contrast with alternatives like x711_tx_simulate, leaving some ambiguity about when to broadcast vs simulate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

With no annotations, the description fully discloses the tool's behavior: uses public RPC eth_call + eth_estimateGas, no wallet/API key needed, estimates cost in USD, enriches with Hive wisdom, and returns specified fields. No contradictions.

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

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

The description is informative but slightly verbose; however, every sentence adds value and it is well-structured with front-loaded purpose. Could be more concise but still highly effective.

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

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

Given 5 parameters and no output schema, the description covers the return structure, integrates with sibling tools, and provides additional context like free tier limits. It is fully sufficient for an AI agent to understand and use the tool.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds minimal extra parameter detail beyond schema (e.g., default chain is base), but does not significantly enhance parameter understanding.

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

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

The description clearly states the tool simulates on-chain transactions on multiple chains before broadcasting, and explicitly distinguishes it from the sibling x711_tx_broadcast by instructing to always simulate first.

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

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

Provides explicit when-to-use context: 'Always simulate before calling x711_tx_broadcast'. Also mentions free tier limit (3 sims/day) which helps manage usage.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Without annotations, the description bears full burden and discloses important behaviors: always returns results, fallback chain, free tier limits (10/day). Could mention rate limits or latency, but current detail is adequate.

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

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

Front-loaded with key info (multi-source, fallback). Sentences are purposeful, though slightly verbose. Could trim redundancy in examples but overall well-structured.

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

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

Given no output schema, the description thoroughly explains return format, sources, and fallback behavior. Covers edge cases (unavailable sources) and usage limitations. Complete for a search tool.

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

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

Schema covers the single 'query' parameter with examples. Description adds no new semantic info beyond schema (same examples). Baseline 3 since schema coverage is 100%.

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

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

The description clearly identifies the tool as a multi-source web search with a specific fallback chain (HackerNews → Wikipedia → DuckDuckGo → Hive). It distinguishes itself from sibling tools by focusing on web search and community knowledge, which is unique among the listed x711 tools.

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

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

Explicitly states best use cases (tech/AI/crypto, current events, documentation discovery) and mentions automatic fallback. Lacks explicit 'when not to use', but the context and sibling tool names imply this is the go-to for general web search.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Given no annotations, the description covers key behaviors: it is free, requires no API key, and works across x402-compliant APIs. It lacks details on error handling or limitations, but is sufficient for the tool's simplicity.

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

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

The description is extremely concise, only two sentences, with no redundancy. Key information is front-loaded.

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

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

The tool is simple with one parameter and no output schema. The description covers input, output structure, and compatibility, making it complete for the use case.

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

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

Schema coverage is 100% with one parameter. The description adds context by specifying the expected input (full response body, JSON object or string) beyond the schema's type definition.

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

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

The description clearly states that it parses HTTP 402 response bodies and returns structured payment instructions. It differentiates itself from sibling tools, which are unrelated to parsing.

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

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

The description implies when to use (when a 402 response is received) and notes compatibility with multiple APIs, but does not explicitly state when not to use or provide alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.