Agent Kiosk — 12-in-1 B2A Intelligence & x402 API

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

With no annotations, the description carries the full disclosure burden; it does add real value by naming the returned artifacts (citation counts, reconstructed abstracts, authors, Open-Access PDF links) and the upstream sources. However it says nothing about latency (the word 'Deep' implies cost without elaborating), rate limits, or whether coverage is deduplicated across the two sources.

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 tight sentences, front-loaded with the purpose and followed immediately by sources and return contents. No filler beyond the mildly redundant 'Deep scientific & academic' pairing.

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 two-parameter search tool with no output schema, the description usefully enumerates what comes back (citation counts, abstracts, authors, OA PDF links) and where results come from, so the missing output schema is largely compensated. It stops short of stating result ordering or relevance ranking, which an agent might reasonably want.

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 both parameters are already fully documented with an example query and a 1-15 default-5 limit. The description adds no syntax, filtering, or range guidance beyond what the schema provides, so the baseline 3 applies.

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?

States a specific operation (queries OpenAlex and arXiv for peer-reviewed papers and preprints) and names the exact data sources, which no sibling tool covers. The verb+resource+provenance combination lets an agent place it immediately against the other intel/search siblings.

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 framing 'Deep scientific & academic research tool' implies the domain of use but gives no explicit when-to-use condition, no exclusions, and no pointer to alternatives such as domain_intel or web_extract for non-academic queries. Usage must be inferred from the domain label alone.

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 must carry the behavioral burden. It does disclose the upstream data source (DeFiLlama), that chain and symbol have defaults (Base, USDC), and that TVL acts as a 'safety filter' — useful context. However, it omits return characteristics (does it return APY, TVL, pool addresses?), whether results are live or cached, any rate limits, and whether min_tvl is a hard exclusion.

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, front-loaded with what the tool is and followed by what it returns and how it is filtered. Minimal waste, though the 'highest APY & yield radar' phrasing is slightly marketing-flavored rather than purely informational.

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?

All four parameters are optional with defaults and fully documented in the schema, and no output schema exists. The description names the result category ('top lending and liquidity pools') but does not describe the returned fields, so an agent cannot anticipate the response shape. Adequate but with a clear gap for a data-returning 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 every parameter (chain, limit, symbol, min_tvl_usd) is already documented with type, range and default in the schema. The description reinforces the filtering semantics (token + chain + TVL) but adds no syntax or interpretation detail beyond what the schema provides, making the baseline 3 correct.

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 states a specific verb and resource: 'Finds top lending and liquidity pools for any token', with concrete examples (USDC, ETH, USDT, WBTC) and the filters it applies (blockchain, minimum TVL). This is clearly a DeFi yield scanner, distinct from every sibling tool (which are search, intel, audit and profiling tools), though the description never explicitly contrasts itself with them.

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?

Usage is implied by the framing — use this when you want the highest-yield lending/liquidity pools for a token, optionally narrowed by chain and TVL. There is no explicit when-to-use statement, no prerequisites, and no named alternative for related questions (e.g. general token intelligence via token_alpha).

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 are supplied, so the description carries the full behavioral burden. It discloses the read-only operations performed, but never states that this is a non-mutating lookup, whether rate limits or quotas apply, how exhaustive subdomain discovery is, or whether the target must be externally resolvable.

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?

A single front-loaded sentence naming the domain, followed by the concrete capability list. Nothing is padded, though the five-item enumeration is dense for a one-sentence description.

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?

With no output schema and no annotations, the description usefully enumerates the return categories (records, provider, tech stack, registration dates, subdomains), which is the essential missing context. Gaps remain around cost, limits, and result depth, but the core is 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?

Only one parameter, and schema coverage is 100% — the schema already documents 'domain' with examples ('stripe.com', 'vercel.com'). The description adds no format or normalization detail (e.g., whether a full URL with path is accepted). Baseline 3 applies.

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 names a specific domain and enumerates five concrete capabilities (DNS record resolution, email provider identification, SaaS stack detection via TXT, RDAP registration lookup, subdomain discovery), so an agent knows exactly what comes back. It stops short of explicitly differentiating itself from siblings like security_audit or github_intel, which also touch domain/company reconnaissance.

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?

There is no when-to-use framing, no prerequisites, and no mention of alternatives such as security_audit or web_extract that overlap in scope. Usage is only inferable from the capability list, which is not the same as 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 carries the full behavioral burden, yet it says nothing about read-only vs. mutating behavior, authentication, rate limits, or whether results are paginated. It lists data categories ('metrics and latest releases') but never discloses operational traits an agent needs before invoking.

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 compact sentences, front-loaded with the resource, and immediately followed by the mode-selection rule. Every clause earns its place with no filler.

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 two-param, no-output-schema tool the core usage is adequately covered, but with no annotations the description should carry safety/behavioral context it omits. What the 'deep metrics' and 'releases' actually return is left unspecified.

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 both params are already documented, and the description's explanation of the query dual-mode largely mirrors the schema rather than extending it. It adds no syntax hints or format nuances beyond what the schema states, so the 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?

Names a specific resource (GitHub repository / open-source data) and the two operating modes (deep repo metrics vs. topic discovery), which lets an agent distinguish it from siblings like package_audit or social_search. 'Competitive intelligence' is slightly abstract about actual return content, so it stops short of a 5.

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?

Clearly explains the two input modes and the condition that selects each: 'owner/repo' for deep metrics, a keyword topic for fastest-growing repos. It does not, however, state when to prefer this over neighboring tools or note any exclusions, so it lacks the explicit alternatives a 5 requires.

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 behavioral burden. It usefully enumerates the extracted fields (name, phone, website, address, hours, GPS), which substitutes for the absent output schema, but says nothing about rate limits, API keys/credits, geographic coverage limits, caching, or result freshness.

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, zero filler. The capability statement leads and the return-field enumeration follows, both front-loaded and immediately actionable.

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 3-parameter lookup tool with no annotations and no output schema, the description covers purpose and return fields adequately. It omits operational context an agent would want — whether the call consumes credits, how location interacts with query, and any result-quality caveats.

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% — all three parameters (query, location, limit with its 1-25 range and default) are documented in the schema itself. The description adds no syntax, format, or example guidance beyond that, so the baseline of 3 applies.

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 gives a specific verb and resource — 'Find local businesses, companies, and places in any city worldwide' — plus the exact data fields returned, so the agent knows precisely what the tool produces. It does not name or distinguish itself from any sibling, but no sibling occupies the maps/places domain, so the gap is minor.

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?

'For lead generation and verification' implies the intended use case but offers no when-to-use vs when-not guidance, no alternatives, and no prerequisites. The agent can infer context but must guess at boundaries (e.g., how this differs from web_extract or social_search for gathering business data).

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 are provided, so the description carries the burden. It does disclose the data source (Google OSV.dev) and the kinds of results returned (vulnerabilities, deprecation status, latest stable version), which is useful. However it omits whether the call is read-only/network-dependent, any rate limits or auth requirements, and how unknown packages are handled.

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 tight sentences with the identifying noun phrase front-loaded ('Open-source package & CVE security scanner'), followed by the concrete data source and outputs. No filler; only minor redundancy between the title-like opener and the rest.

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 three-parameter lookup tool with no output schema, the description covers what it queries, where it queries, and roughly what comes back. Only the operational details (read-only nature, failure modes for unknown packages) are 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 description coverage is 100%, and each field is already documented (name examples, version optionality, ecosystem enum). The description's mention of npm/PyPI/Rust mirrors the enum but adds no default or format detail beyond the schema, so baseline 3 applies.

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?

States a specific verb and resource: an open-source package/CVE scanner that checks npm, PyPI, and Rust crates against Google OSV.dev. This is concrete and distinguishable from siblings like domain_intel or github_intel, though it never explicitly contrasts itself with the nearby security_audit tool.

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?

"For coding agents" gives loose context for who would call it, but there is no statement of when to prefer this over security_audit or the other intel tools, and no exclusions or prerequisites (e.g., offline packages, private registries). Usage must be inferred from the purpose.

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, and it usefully discloses the providers and that values are live real-money implied probabilities expressed in percent. It says nothing about freshness/latency, rate limits, or whether odds from the two sources are merged or returned separately, which is a real gap for a market-data 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?

A single front-loaded sentence leads with the verb and resource, wastes no words, and packs source, data type, and domain coverage into one readable line.

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 two-parameter, annotation-free read query with no output schema, the description conveys the essential idea of the return (odds plus implied probability percentages) and where the data comes from. It stops short of describing result shape, freshness, or multi-source handling, which are minor but not 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?

Schema description coverage is 100%, so both 'query' and 'limit' are already documented in the schema, and the description adds no syntax, example, or range detail beyond it. Baseline 3 applies when the structured schema does the heavy lifting.

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?

States a specific verb (Query) plus resource (live prediction market odds and real-money implied probabilities) and names the two data sources, Polymarket and Manifold Markets. The domain enumeration (crypto, macro, political, tech) also separates it cleanly from siblings like defi_yields, token_alpha, or social_search, so an agent can pick it without opening the schema.

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 domain list ('any crypto, macro-economic, political, or technology event') gives clear context for when the tool applies. However, it names no alternatives and includes no when-not guidance, so the agent must still infer that this is not the tool for on-chain yields or raw token metrics.

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 burden and does well: it discloses the exact analyses performed (honeypot, buy/sell tax, mintable risk, DEX liquidity, phishing, wallet drainers, prompt injection) and the upstream data sources (GoPlus, DexScreener). It omits operational traits such as latency, rate limits, read-only confirmation, and how unsupported targets behave.

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 tight sentences with the scope up front and the concrete check list second; each clause carries information. The opener's "for AI agents" framing is mildly redundant in an MCP context but costs little.

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 2-param, read-style tool with no annotations and no output schema, the description covers targets, per-target checks, and data sources adequately. An agent still lacks any sense of response shape or timing, but nothing needed to select and call the tool is 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 description coverage is 100% and both parameters are fully described there, including the chain enum default. The description restates the two target forms without adding format, validation, or chain-compatibility detail 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?

States a specific verb (audits) and two precisely delimited resources (0x smart contracts/tokens vs web URLs), then enumerates the checks performed on each. An agent can distinguish this from sibling tools like package_audit, domain_intel, and web_extract without opening any schema.

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?

"Pre-flight security and scam radar" gives clear usage context: run this before interacting with or transacting against a contract/URL. It does not, however, name alternatives or exclusions (e.g., when to prefer domain_intel for URL reputation), so it stops short of full routing 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 carries the full burden and does disclose meaningful traits: the return format ('clean structured JSON and LLM-ready Markdown') and the absence of auth friction or rate-limit blocks ('without rate-limit blocks or API keys'). It does not cover pagination, sorting, or result recency semantics, so it stops short of fully rich 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, front-loaded with purpose and followed by output/access characteristics; no filler. Efficient, though the second sentence packs three separate claims that could be trimmed.

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 three-parameter search tool with full schema coverage, no output schema, and no annotations, the description covers purpose, sources, return format, and auth/rate-limit expectations. Only minor gaps remain around result ordering and pagination depth.

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 limit, query, and platform are already documented with defaults and an example. The description adds nothing about syntax or defaults, and its mention of 'tech forums' is not represented in the platform enum (all/reddit/hn), which is mildly ambiguous rather than clarifying. Baseline 3 applies.

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?

States a specific verb ('Search') plus resource ('live social media discussions') and enumerates the sources (Reddit, HackerNews, tech forums). This implicitly separates it from sibling academic_search, but it never names the alternative explicitly, so differentiation is left to inference.

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 phrase 'for any topic, product, or trend' implies the use case, but there is no explicit when-to-use guidance, no when-not-to-use, and no mention of sibling search tools. Usage is implied rather than directed.

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 are provided, so the description carries the full burden. It signals a read-only analytics operation and a 'real-time' freshness claim, which is useful, but it says nothing about rate limits, latency, coverage gaps per chain, or what happens when a ticker is ambiguous across chains.

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 dense sentences that front-load the domain and then enumerate the analytic outputs. No filler, though the trailing metric list reads as keyword stuffing rather than agent guidance.

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?

With no output schema and no annotations, the description does most of the work by enumerating the returned metrics (pressure, liquidity, velocity, FDV, pools). It stops short of stating response shape or chain-coverage caveats, but an agent has enough to call it 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 description coverage is 100%, so both parameters are already documented in the schema (query with ticker/address examples, chain enum with default). The description's 'any token ticker or contract address' merely restates the query param and adds nothing about the chain parameter.

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?

Names a specific capability set (order-flow analysis: buy/sell pressure, liquidity depth, volume velocity, FDV, top DEX pools) and the input domain (token ticker or contract address). An agent can tell this apart from analytics-only siblings like defi_yields, though no sibling is explicitly named.

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?

There is no explicit when-to-use, when-not-to-use, or alternative routing. The description only implies usage ('for any token ticker'), leaving the agent to guess how this differs from defi_yields or wallet_profiler when the question is token-level vs. protocol-level.

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 must carry the behavioral load. It usefully discloses the returned fields (balances, nonce, tier, EOA/contract detection) but says nothing about read-only nature, rate limits, auth/API-key needs, rate of refresh, or behavior for unsupported chains or invalid addresses.

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?

A single dense sentence that front-loads the primary capability (chains covered) and then the returned metrics. Every clause carries information; only minor trimming would be possible without losing detail.

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?

With no output schema, the description correctly enumerates the return contents, which is the main thing an agent needs for a single-address read tool. It stops short of covering error/edge behavior, but nothing critical for correct invocation is 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?

Only one parameter and the schema already fully documents it ('0x... 40-hex EVM wallet or contract address to profile'). The description adds no syntax, format, or constraint detail beyond the schema, so the 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?

Names a specific verb ('profiler') and resource ('multi-chain whale & wallet portfolio') and enumerates the exact chains covered (Base, Ethereum, Arbitrum, Polygon). It is unmistakably distinct from every sibling tool (searches, yields, audits), so an agent can select it without opening the schema.

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 the use case (profile a wallet/whale on supported chains) but never states when to choose it, when not to, or any prerequisite. No alternative tool is named, and no exclusion (e.g. non-EVM addresses) is given, so usage must be inferred.

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 are provided, so the description carries the full behavioral burden. It usefully discloses that it scans for hidden prompt injection traps, but it omits what happens when traps are found, whether scanning can be disabled, and any auth/rate-limit behavior.

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?

A single, front-loaded sentence with no wasted language. The core extraction purpose comes first and the prompt-injection scanning detail follows naturally.

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 explains the primary output (Markdown) and a security feature, but it does not describe the return shape of the prompt-injection scan, error behavior, or limitations. With no output schema and no annotations, more context would be expected.

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 both parameters are already documented. The description adds no syntax, format, or behavior details beyond what the schema provides, making the baseline 3 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?

States a specific verb and resource: extract clean Markdown from a web URL. This clearly distinguishes it from search/intel siblings like academic_search or security_audit, though it does not name any sibling explicitly.

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?

Usage is implied by the stated purpose: use it when you need LLM-ready Markdown from a URL. There is no explicit when-to-use guidance, no exclusions, and no mention of alternatives, so it sits at minimum viable.

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