Saylor Innovations: pay-per-call data

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

Goes well beyond the read-only/idempotent annotations: discloses the $0.02 cost, that payment is USDC on Base, that the response returns POST instructions rather than a paid result, that baselines expire after seven days, and that reads are block-pinned. These are exactly the operational traits an agent needs before invoking a paid 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?

Front-loaded with the core purpose, then constraints, then payment/logistics; every clause carries information. It is dense and the telegraphic final fragments ('USDC on Base. Costs $0.02.') read abruptly, but nothing is wasted.

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 carries the return burden and does so reasonably — 'field-level evidence and unknowns' plus the note that a POST instruction set is returned instead of results. It is complete for invocation; only the exact shape of the evidence/unknowns payload is left implicit.

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 documents address, pool (with its token0()/token1() constraint) and baseline. The description restates the seven-day baseline signing and same-pool pool semantics but adds no format or syntax detail beyond the schema, 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 resource and method: a Base token change report that compares signed seven-day baselines against block-pinned owner, pause, proxy and supply reads. It also names what it deliberately does not do (no honeypot simulation, sell-tax estimate, or safety guarantee), which separates it from check_token_security, though it never names a sibling directly.

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?

Implied workflow is clear — pass a baseline from a previous paid response for the same address/pool to get a change report — and the explicit exclusions ('No honeypot simulation, sell-tax estimate...') tell the agent when not to use it. It stops short of naming the sibling to use instead for those excluded questions, so it is not a full 5.

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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is known. The description adds useful behavioral facts beyond the annotations: the output contains top 20 holders, percent of supply, per-wallet insider labels, and that the tool costs $0.01 with a free sample URL. It does not describe response envelope or failure modes, but those are minor for this read-only single-parameter endpoint.

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 dense but efficient: it front-loads the purpose, lists the exact output fields, distinguishes the sibling tool, and adds pricing and a free sample link—all in a few clauses. There is no filler or repeated schema 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?

For a simple read-only endpoint with one documented parameter and no nested output, the description covers what an agent needs: what the tool returns, how it differs from the related tool, the cost, and an example sample. The lack of an output schema is mitigated because the description names the output contents explicitly.

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 the only parameter, mint, is already documented as a Solana mint address in base58. The description adds no new parameter-level details beyond reinforcing the Solana context, so the schema carries the weight. 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?

The description states a specific verb ('Check'), a specific resource ('holder concentration' for a Solana token), and quantifies the output (top 20 holders with percent of supply and RugCheck's insider label). It also explicitly contrasts itself with check_token_security, so an agent can distinguish the tool immediately 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 names the key alternative, check_token_security, and explains the difference: this tool gives per-wallet top-20 detail rather than a rolled-up top-10 percentage. That gives an agent a clear basis for choosing between the two. It does not explicitly list all when-not-to-use conditions, but the comparison is enough for practical routing.

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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, covering safety. The description adds the non-obvious billing detail 'Costs $0.005,' which is valuable behavioral context beyond the structured 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?

The entire description is one efficient sentence that front-loads the purpose, enumerates the categories, mentions socials, and discloses cost. No filler or redundant restatement of the name or schema.

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 one-parameter, read-only, idempotent tool, the description is nearly complete: it states the output domain, lists categories, notes socials, and gives the cost. The only minor gap is that the exact structure of the socials output is not described, but this is acceptable without an output schema for a simple classification 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%, with the single 'mint' parameter already documented as a Solana base58 address. The description adds no additional parameter-level meaning, so the baseline score 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 clearly states what the tool does: classifies a token into one of ten named narrative categories and provides socials. This distinguishes it from sibling token tools like check_token_price and check_token_holders, which focus on different 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 intended use is implied by the description: call this when you need a token's meme/narrative category and socials. However, it does not explicitly say when not to use it or how it differs from related tools such as check_token_snapshot, so the agent must infer the selection criteria.

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?

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds valuable behavioral context: data comes from two independent sources, the spread is reported and itself meaningful, and there is a specific cost. No contradiction with 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?

Two tight sentences with no filler. The core function is front-loaded, and the cost/polling note is placed second as a natural usage qualifier. Every word earns its place.

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 one-parameter read-only endpoint with rich annotations, the description is nearly complete: it states what is returned (price, liquidity, spread), the source behavior, and cost. It does not specify units or output shape, but the absence of an output schema and the tool's simplicity make this 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?

Schema coverage is 100% and the only parameter, mint, is already described as a Solana mint address. The description adds no new parameter-level detail, so the baseline of 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?

The description names a specific resource (Solana token), a concrete operation (fetch price and liquidity), and a distinctive differentiator (two independent sources with reported spread). This clearly separates it from siblings like check_token_security or get_price_history.

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 gives clear context for when to use it: it is the cheapest endpoint and 'built for polling,' implying repeated low-cost checks. It does not explicitly name alternatives or exclusion criteria, but the polling/cost framing is strong usage 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?

Annotations already mark the tool read-only and idempotent, and the description adds valuable non-inferable behavior: the $0.01 cost, the free sample URL, and the payment-requirements/URL-to-call flow. No contradiction with the annotations exists.

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 the core purpose, followed by a compact list of outputs and then the cost/sample/payment details. Every sentence carries distinct information with no filler 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?

With no output schema, the description covers the main report contents, cost, sample endpoint, and the unusual paid-URL flow. The only weakness is ambiguity about whether the immediate response is the full report or a payment redirect, but an agent still has enough information to call the tool and interpret its result.

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 documents the only parameter, mint, at 100% coverage with 'Solana mint address (base58).' The description reinforces that the target is a Solana token but does not add meaningful parameter-level detail beyond the schema, so the baseline score of 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?

The description names a concrete function ('rug risk check') and the resource ('Solana token'), then enumerates specific report contents such as mint/freeze authority, LP lock, holder concentration, RugCheck risks, and a security score. This clearly distinguishes it from sibling tools focused on holders, narrative, or price.

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 'is this token safe to buy?' framing establishes a clear pre-purchase safety-assessment use case, which is distinct from the related token tools. It does not explicitly name alternatives or exclusions, but the context is sufficient for an agent to decide when to invoke it.

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 annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile, so the bar for additional disclosure is lower. The description adds real behavioral context beyond the annotations: the call costs $0.01 (a paid operation, which an agent should know before invoking), and the data is 'sampled' (age) with a 'failed-transaction rate' and behavioural flags, implying heuristic rather than exhaustive computation. No contradiction with annotations exists.

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 sentence with the core subject ('On-chain profile for a Solana wallet') front-loaded, followed by a compact enumeration of report contents and a closing cost note. Every clause earns its place, and the cost disclosure is important enough to include. No filler or 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 single-parameter read tool with full annotation coverage and 100% schema coverage, this is largely complete. Because there is no output schema, the description compensates by enumerating what the report contains (SOL balance, token positions, recent activity, etc.), giving the agent a sense of the return payload. The main gaps are the unspecified nature of 'behavioural flags' and the output format, but these are minor relative to the annotated safety profile and fully documented parameter.

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%: the single required parameter `address` is already described as a 'Solana wallet address (base58)' in the schema. The description only reinforces that the wallet is Solana-based and does not add syntax, format, or validation detail beyond the schema. With full schema coverage, the baseline of 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?

The description states a clear resource (Solana wallet) and a specific deliverable (an on-chain profile report) and enumerates its contents concretely: SOL balance, token positions, recent activity, sampled age, failed-transaction rate, and behavioural flags. It differentiates clearly from the token-focused siblings (check_token_holders, check_token_price, etc.) since it targets a wallet address rather than a token. An agent can immediately tell this apart from the sibling set.

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 or when-not-to-use guidance, and no named alternatives or exclusions. The usage context is only implied by the content (a wallet-level profile), which an agent can infer but is not told. It does not state, for example, 'for a token-level report, use check_token_* instead.' This is adequate but leaves the selection logic unstated.

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?

Annotations already carry readOnly, idempotent, and non-destructive signals, so the description need not re-state them. It adds the affordable per-mint price and lighter payload comparison, but it does not disclose output shape or any rate-limit/error behavior beyond what the annotation hints provide.

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 are efficient and front-load the action and scope. The final sentence somewhat repeats the one-request idea already in the first sentence, which keeps it from a 5.

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, read-only batch tool, the description covers purpose, range, token format, pricing, and when to use it. With no output schema, it could say a bit more about what the returned scores contain, but the main context an agent needs to call it correctly is present.

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 the schema already documents mints as Solana mint addresses in base58, 2 to 10. The description repeats the 2-10 range but adds no new param-level meaning, 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 begins with a specific verb-plus-resource: produces Watchdog scores for 2-10 Solana tokens side by side in one request. It also names get_token_snapshot as a sibling alternative, so an agent can distinguish it from similar token 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?

It clearly frames the tool for screening a watchlist in one call and compares pricing to get_token_snapshot, implying when the lighter payload is preferable. It stops short of explicitly saying when not to use it or naming other alternatives, so not a full 5.

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?

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, open-world), and the description adds genuinely new context beyond them: heuristic matching caveats, the 'leads not proof' accuracy warning, and a per-call cost of $0.003. It stops short of describing error behavior or how candidate entities are ranked.

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 short sentences, front-loaded with the verb and output, followed by the caveat and the price. No filler, and the most important constraint (leads, not proof) is stated before the cost.

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 does describe the return shape (page metadata, candidate GLEIF entities, LEI, status), which is the main burden. The gaps are the undocumented limit parameter and the absence of any note on how many candidates are returned or ranked.

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 only 50%: domain is documented in the schema, but limit (default 5, max 20) has no description anywhere, and the description says nothing about it or about pagination/result-count behavior. The description therefore does not compensate for the undocumented 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?

States a specific verb (resolve) and resource (public company domain) and enumerates the concrete outputs: page metadata plus candidate GLEIF legal entities with LEI and status. This differentiates it from the overlapping sibling get_page_metadata by making the legal-entity enrichment explicit.

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?

Gives a clear use context ('leads for due diligence') and a firm limitation ('not proof of ownership or authorization'), plus a cost signal. It does not name an alternative tool or state when to prefer a plain page-metadata call, so the routing guidance is contextual rather than explicit.

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?

Annotations already declare read-only, idempotent, non-destructive safety, and the description still adds real behavioral context: SSRF guarding, refusal of private addresses / non-HTML / error pages, that refusal happens before payment, and an explicit $0.005 cost. These are exactly the traits an agent needs and none of them come from structured fields.

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, front-loaded with what is fetched and produced, followed by guardrails and price. Every clause (SSRF-guarded, bounded, before payment, cost) earns its place with zero 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?

With no output schema, the description usefully enumerates the returned fields (title, author, date, canonical URL, links, metadata), which is strong. It is slightly incomplete on how max_chars truncates output and on its relationship to get_page_metadata.

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 67% (url and links documented, max_chars not). The description lists "links" among outputs, loosely hinting at the links flag, but gives no guidance on max_chars or truncation behavior, so it neither compensates fully nor is empty. Baseline 3 fits.

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+resource: fetch a public URL and extract main content as clean Markdown, even enumerating returned fields. However, it never distinguishes itself from the sibling get_page_metadata, which an agent could easily confuse with this 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?

"Designed for agent research" and the refusal rules (private addresses, non-HTML, error pages) imply when it applies, but there is no explicit when-to-use versus when-not, and no named alternative such as get_page_metadata.

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?

Annotations already declare the safety profile (readOnly, idempotent, openWorld, non-destructive), so the bar is lower. The description adds genuinely useful context beyond them: a concrete cost ($0.002) and a caveat that distances use site reference points and are not a property-safety determination.

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 zero filler; the resource and scope are front-loaded ahead of the cost and caveat. Every clause earns its place.

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 usefully enumerates the returned fields (identifiers, cleanup status, hazard-ranking score, dates, distance, source profiles) and discloses cost and a disclaiming caveat. Only the radius parameter's semantics are left under-explained.

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 only 25%, so the description must compensate, yet it adds little beyond 'coordinates or a place' and an implicit nod to 'distance'. It never explains radius_km units, its default of 10, or its 1-50 bounds, leaving the parameter semantics largely to the schema's min/max.

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 ('Find') and resource ('U.S. EPA National Priorities List sites') with clear scope ('near coordinates or a place'). It even enumerates the returned fields, and no sibling covers the same subject, so an agent can select it without ambiguity.

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 two input modes (coordinates vs a place), which is useful context, but it never states when to prefer this tool over related siblings like get_radon_potential or geocode_address, nor any exclusions. Usage is implied rather than guided.

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?

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description adds real behavioral context beyond them: a $0.002 cost per call, the data source, the returned attributes, and explicit accuracy limits. It does not mention rate limits or failure modes for unresolved 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?

Two sentences, both front-loaded: the action and source come first, then outputs/cost/caveats. Every clause carries information the agent cannot get from the structured fields, 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?

There is no output schema, so the description proactively enumerates return fields (display names, country/region/type, retrieval time, attribution) plus cost and accuracy boundaries. For a single-parameter lookup tool this is complete enough to call blind.

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 description coverage is 100%, so the schema already documents 'address' fully. The description repeats the accepted inputs (address, place name, landmark) without adding length, format or ambiguity guidance, so this is baseline-adequate rather than additive.

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 (resolve) and resource (street address, place name or landmark → ranked coordinates) and names the backing service (OpenStreetMap Nominatim). No sibling tool does geocoding, and an agent can tell instantly what this produces.

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 closing sentence delimits the scope: results are 'not legal boundaries, navigation routes or survey-grade coordinates,' which tells the agent when this tool's output is unsuitable. It does not name an alternative tool or a precise triggering condition, so it stops short of explicit when/when-not routing.

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?

Annotations already cover readOnly, openWorld, idempotent, and non-destructive hints, so the description is not required to repeat them. It adds valuable behavioral details: the cost ($0.003), the return of payment instructions for the Saylor endpoint, and the lack of a provider account requirement. These are beyond what annotations provide and are essential for an agent to decide whether and how to invoke the 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 sentences, each earning its place: the first states the purpose and output fields, the second adds cost and access requirements. The purpose is front-loaded, and there is no redundant or filler content.

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 a single parameter, and the schema and annotations cover safety and parameter details. The description adds cost and authentication context, which is important. It does not describe the return format, but since there is no output schema, that might be a gap; however, the description hints at 'payment instructions' and the data fields, which is sufficient for an agent to understand the result. Slight lack of return structure prevents a 5.

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 description coverage is 100%, meaning the schema already fully documents the 'country' parameter, including an example ('US or GB') and the note that aggregates are excluded. The description only repeats 'two-letter country code' and does not add any additional semantics, formatting details, or edge-case guidance. Baseline 3 applies because the 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?

The description uses a specific verb ('Resolve') and clearly specifies the resource (two-letter country code) and the expected output (World Bank name, region, capital, income group, lending classification). It distinctly positions this tool as a data-enrichment lookup, which differentiates it from sibling tools like get_economy_report or get_fx_report without needing to name 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?

The description mentions 'for data enrichment,' which implies a use case but does not explicitly state when to use this tool versus alternatives or when not to use it. There is no guidance on exclusions (e.g., aggregates are excluded, but that's in the schema, not the description). The usage context is present but not fully articulated.

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?

Annotations already mark the tool as read-only, open-world, idempotent, and non-destructive, and the description adds valuable context beyond those: it costs $0.003, returns payment instructions for the Saylor endpoint, and requires no provider account. This gives an agent critical cost and invocation context without contradicting the 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?

Three sentences, each earning its place: the first states the core purpose, the second clarifies the tool's boundary, and the third communicates cost and access requirements. It is front-loaded and free of redundant wording.

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 a single parameter, no output schema, and comprehensive annotations, the description provides everything needed to call the tool correctly: the input, the expected output content, the cost, and the payment/access behavior. No critical information 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 the single 'cve' parameter is already documented with an example (CVE-2021-44228). The description reinforces that the parameter identifies a CVE but does not add substantive new parameter semantics 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 uses a specific verb 'Resolve' with a clear resource, CVE, and enumerates the concrete outputs: description, status, CVSS metrics, weakness identifiers, and vendor/reference links. It also distinguishes itself from security-scanning tools by stating 'This does not scan a system or prove safety.'

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 clearly indicates when to use the tool: when a CVE identifier needs to be resolved into published vulnerability details. It also gives an explicit when-not boundary by stating it does not scan a system or prove safety, though it does not name a specific alternative tool.

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?

Annotations already declare read-only, idempotent, non-destructive behavior; the description goes beyond them by disclosing the $0.003 cost, the returned payment instructions, and that no provider account is required. These are exactly the operational traits an agent needs before invoking the tool. No contradiction with 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?

Four short sentences, front-loaded with the core purpose and followed by essential operational facts: exclusions, cost, and authentication context. Every sentence earns its place; the cost disclosure is material information for tool selection, not 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 single-parameter tool with rich annotations and no output schema, the description enumerates the returned artifact categories and their exclusions while also covering cost and account requirements. Nothing an agent needs in order to call it correctly 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 coverage is 100%: the sole parameter is documented with its optional https://doi.org/ prefix. The description merely echoes that the parameter is a DOI without adding new format or usage nuance. With complete schema coverage, the baseline of 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?

The description names a specific action ('Resolve a DOI') and resource, then lists the exact delivered outputs: normalized metadata, citation text, identifiers, and source links. The explicit exclusion of abstracts and full text sharpens the boundary against document-retrieval tools. Among 27 sibling tools, the DOI/citation scope is unambiguous.

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 intended use case—having a DOI and needing citation-level metadata—is clearly implied by the first sentence, and the abstract/full-text exclusion signals what this tool is not for. However, no alternative tool is named and no explicit when-not condition is stated, so an agent must infer routing from DOI-specific vocabulary rather than being 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?

The description adds meaningful behavioral details beyond annotations: it discloses a cost of $0.003, returns payment instructions for the Saylor endpoint, and notes no provider account is required. It also describes the output contents (years, units, source links, missing values), which enriches the annotation-only safety profile.

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 two sentences with no waste. The core action is front-loaded, followed by cost and output details. Every sentence adds value.

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 the output contents, cost, and authentication requirements. Since there is no output schema, it adequately explains what the agent will receive. It could be more complete by specifying what 'recent' means, but that is minor.

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 documents the country parameter well with an example and exclusion of aggregates. The description does not add further meaning about the parameter itself, so it stays at the baseline for full schema coverage.

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 retrieves up to five recent annual observations for population, GDP, and consumer-price inflation. It specifies the resource (economic indicators) and verb (get), but does not explicitly differentiate from sibling tools like get_country_report, which may overlap.

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?

No guidance is provided on when to use this tool versus alternatives. The description does not mention any exclusions or conditions, leaving the agent to infer usage solely from the content.

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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds useful behavioral context: it returns price and payment instructions, is free, and the agent then pays and receives data. It also clarifies the input is a fully-qualified URL from search_endpoints, adding value beyond annotations without contradicting them.

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 two sentences with zero fluff. It front-loads the purpose ('Given an endpoint URL from search_endpoints...'), states the return contents, and adds the 'Free.' note. Every sentence earns its place; it is concise and 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?

The tool is simple (one parameter, no output schema), and the annotations already cover safety and idempotency. The description explains the return content (URL, price, payment instructions), the source of the input, and the overall flow. Nothing an agent needs to call it correctly 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 coverage is 100% (the url parameter is fully described as a full https URL including query parameters). The description adds meaning by specifying the URL should come from search_endpoints and have its parameters filled in, which clarifies the expected source and format. This enriches the parameter's semantics 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 states a specific verb ('returns'), a clear resource (endpoint price/payment instructions), and a distinct flow: given an endpoint URL from search_endpoints, it provides the exact resource URL, price, and x402 payment instructions. It also mentions 'Free.' to signal cost. This clearly differentiates from search_endpoints (which finds endpoints) and other sibling tools that check tokens or reports.

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 'Given an endpoint URL from search_endpoints', which establishes the tool's place in the workflow: use after search_endpoints to obtain payment details. It implies the usage context (post-search, pre-payment) and the flow of paying over HTTP. However, it does not explicitly state when not to use it or name alternatives, but the context is clear enough.

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?

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description adds genuinely non-derivable context: live chain-head semantics, the returned block number, an explicit cost of $0.003, and a scope disclaimer of what the read does not constitute.

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 with no filler; the chain list, return contents and cost are front-loaded, and the negative-scope clause clarifies boundaries in the same breath.

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 carries the return-shape burden and does so well by naming the payload fields and the block number. Combined with annotations covering safety and the explicit pricing, an agent has nearly everything needed, though wallet-format semantics remain thin.

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 33%, with only 'tokens' documented in the schema, so the description must compensate. It enumerates the six supported chains (mirroring the enum) and mentions requested ERC-20 balances, but adds nothing about the wallet address format or the 20-token cap beyond what the schema already encodes.

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 concrete verb (read) and resource (an EVM wallet across six named chains) and enumerates the returned payload (native balance, ERC-20 balances, token metadata, block number). It is clearly differentiated from token-oriented siblings like get_token_snapshot, though it never contrasts itself with the overlapping check_wallet sibling.

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 point-in-time framing and the negative scope ('not custody, valuation, proof of reserves or transaction authorization'), but there is no explicit when-to-use guidance or reference to an alternative tool. The agent must infer when this beats check_wallet.

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?

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, open-world), so the bar is lower. The description adds genuinely non-obvious behavioral facts: a $0.002 cost and payment instructions for the live x402 endpoint, plus explicit execution-risk warnings – none of which are inferable from structured fields.

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 the primary resource before the feature enumeration. Dense but nearly every clause maps to real output; it trends toward a feature dump rather than crisp prose but wastes 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?

With no output schema, the description helpfully enumerates return contents (rates, venue intervals, annualized comparisons, provenance, risk warnings) and the payment flow. It is complete on the response side; the gap is undocumented parameters, which slightly undermines call correctness.

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 0%, so the description must carry parameter meaning, but it never explains sort, limit, or minOiUsd. Phrases like 'open interest filters' and 'annualized comparisons/spread signals' only faintly gesture at minOiUsd and the sort values without stating formats or defaults.

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 (perpetual-futures funding rates) with a concrete scope (Hyperliquid, Binance, Bybit) and enumerates the distinct outputs it produces. No sibling tool covers funding rates, so the resource is unambiguous.

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 the agent what it will get (rates, annualized comparisons, spread/arbitrage signals) and implies the cross-venue comparison use case, but never states when to prefer it or what conditions precede a call. No exclusions or alternatives are offered, leaving usage to inference.

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?

Annotations already cover read-only/idempotent/non-destructive behavior, and the description adds meaningful operational details: the call costs $0.003, no provider account is required, and the data is ECB-sourced. 'Returns payment instructions for the Saylor endpoint' is an odd disclosure but does not contradict the annotation hints.

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 short and front-loaded with the main purpose; every sentence carries a distinct fact (scope, non-quote status, free source, cost, auth). The 'Returns payment instructions' sentence is the least cleanly tied to the rest but is not 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 one-parameter, read-only, annotated tool this is nearly complete: the description covers what is returned (rates, observation date, currency codes) and how the call is constrained. Without an output schema it could specify the response shape more precisely, but the information an agent needs to select and invoke it is present.

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 only parameter, 'base', is already described as a three-letter ECB-supported currency code. The description's 'per one unit of a base currency' clarifies rate semantics slightly, so it earns the baseline 3 but adds little 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 opening sentence names a specific verb ('Get') and resource ('daily reference exchange rates per one unit of a base currency'), and the disclaimer 'Not executable trading quotes' sharpens the scope. It is clear enough to distinguish from sibling report tools by resource type, though it never names a sibling directly. The later phrase 'Returns payment instructions' slightly muddies the core function.

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 gives useful context: it is for reference rates, not executable quotes, and notes the same ECB data is available free from ecb.europa.eu. However, it does not give explicit when-to-use guidance or point to an internal alternative among the siblings; usage is mostly implied by the tool name and first sentence.

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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, so the tool is known to be safe and non-mutating. The description adds substantial behavioral detail: free guides return the full body immediately, paid guides return payment requirements (a URL) instead of the body, and a PAYMENT-SIGNATURE header is needed to complete payment. It also specifies the cost and payment networks. This goes far beyond the annotations and is crucial for correct invocation.

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 three sentences with zero wasted words. The core purpose is front-loaded ('Fetch a guide by id'), followed by the free/paid distinction and payment flow. Every sentence adds essential information, and the structure is logical and scannable.

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 fully explains what the tool returns in both cases (free vs paid), including the payment completion step. It covers the cost, payment methods, and the required header, which is all an agent needs to correctly invoke the tool and handle the result. Nothing critical 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?

There is only one parameter, 'id', and the schema already describes it as 'Guide id, from search_guides' with 100% coverage. The description repeats the purpose ('by id') but adds no new format, syntax, or constraints beyond the schema. Baseline of 3 is appropriate since the schema fully documents the 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?

The description states a specific verb ('Fetch') and resource ('a guide by id'), clearly distinguishing it from search_guides which finds guides. It also clarifies the free vs paid distinction, so an agent knows exactly what this tool does and how it differs from 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 description implies usage: use this when you have a guide id (from search_guides, as noted in the schema). It provides clear context about free vs paid behavior and how to complete payment, but it does not explicitly state exclusions or name alternatives like 'use search_guides to find guides first.' Still, the context is clear enough for correct selection.

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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable behavioral context: the tool costs $0.003, returns payment instructions for the Saylor endpoint, and requires no provider account. This goes beyond the annotations without contradicting them.

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 two sentences with no wasted words. It front-loads the primary purpose and return contents, then adds cost and authentication context. Every sentence contributes 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?

For a simple one-parameter, read-only tool, the description is largely complete: it states the result fields, the limit of five packages, the cost, and the account requirement. The only minor gap is that 'Saylor endpoint' is not explained, but this does not prevent correct invocation.

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 fully documents the single parameter 'q' with a description of 'Search terms, 2–160 characters.' The tool description adds no additional parameter semantics beyond implying that 'q' is the search query for npm packages, so the schema carries the burden.

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 function: finding up to five npm packages with specific fields like versions, descriptions, and registry ranking scores. It is specific about the resource (npm packages) and the action, but it does not explicitly differentiate itself from the sibling get_package_report, leaving some ambiguity.

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 use for npm package discovery and provides useful context about cost and no provider account being required. However, it does not state when to prefer this tool over alternatives or mention any exclusions, so usage guidance is only implied rather than explicit.

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?

Annotations already declare readOnlyHint and idempotentHint, covering the safety profile. The description adds significant extra context: the cost ($0.003), payment instructions for the Saylor endpoint, and the explicit statement that it does not download or execute package code. This goes beyond what annotations provide.

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 two sentences with zero waste. The core purpose is front-loaded, followed by essential caveats (cost, payment, safety). It is concise and well-structured, earning every word.

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 read-only tool with no output schema, the description covers all necessary aspects: what data is returned, cost, payment process, and safety. It is complete for an agent to invoke correctly without additional assumptions.

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 fully describes the 'name' parameter with a clear explanation of the expected format, so the description adds no parameter-specific meaning. Since schema coverage is 100%, a baseline of 3 is appropriate, and the description does not compensate further.

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 inspects the latest npm package release and enumerates specific details (version, license, dependency counts, engine requirements, deprecation notice, source links). It distinguishes itself from sibling token/wallet tools and even clarifies it does not execute code, making the purpose unambiguous.

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 usage when one needs npm package release information, but it does not explicitly contrast with alternatives like get_npmsearch_report or mention when not to use this tool. There is no exclusion or alternative guidance, so the agent must infer the appropriate context.

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?

Annotations already declare read-only, idempotent, open-world, non-destructive behavior, so the safety bar is already met. The description adds genuinely new information beyond that: SSRF guarding, that the full body is deliberately omitted, and a concrete cost of $0.001 per call — all useful for invocation decisions.

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 extracted fields and then append constraints and cost. No filler, every clause carries information the agent needs.

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 exists, but the description enumerates the returned fields, so the agent knows the shape of the result. Security posture and cost are both disclosed, leaving nothing essential unstated for a single-parameter read 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?

With a single parameter at 100% schema coverage, the schema already documents the URL and its http(s) pattern. The description's 'public HTTP(S) page' phrasing mirrors the schema constraint rather than adding format or syntax detail, 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?

States a specific verb (Extract) and resource (web page metadata) and enumerates exactly which fields are returned (title, description, OG image/site name, author, date, canonical URL, language). The clause 'does not return the full body' implicitly separates it from the sibling extract_webpage_markdown, so an agent can pick between the two without opening either 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?

Gives clear conditions: a public HTTP(S) page, no full body returned, SSRF-guarded. The exclusion of full-body content implies when to prefer extract_webpage_markdown, but that sibling is never named, so the routing guidance stops short of explicit 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?

Annotations already declare readOnly/openWorld/idempotent/non-destructive, and the description adds substantial behavioral context beyond that: a hard cost of $0.003, payment instruction handling, no provider account requirement, and a clear metadata-only boundary. This fully discloses what the agent and user should expect from the call.

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 earning its place: the first states the core capability and output fields, the second sets the scope boundary (metadata not full text), and the third covers cost and authentication. Front-loaded and free of 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 single-parameter read-only tool with no output schema, the description covers everything an agent needs: purpose, result shape, result limit, cost, payment instructions, and auth requirements. Nothing material 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%, so the q parameter is already fully documented as 'Search terms, 2–160 characters.' The description adds a result limit ('up to five') but no additional parameter detail, so a baseline of 3 is appropriate since the 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?

The description states a clear verb ('Find'), a specific resource ('publications'), and concrete return fields (DOI identifiers, authors, publication years, citation counts). It also distinguishes itself from related report tools by specifying the publication domain and the metadata-only scope.

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 gives clear context for when to use the tool: when publication metadata is needed, and explicitly says it does not return abstracts or full text, which hints at when not to use it. It does not name alternatives, but the boundary it draws is enough for an agent to route correctly among the many report 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?

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the description doesn't repeat those. It adds useful behavioral context: it pulls from the highest-liquidity pool and costs $0.01, which are not in annotations. No contradiction with 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?

The description is two sentences with zero fluff. It front-loads the core purpose, then adds the unique differentiator and cost. Every sentence earns its place.

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's simplicity (3 params, all documented) and that annotations cover safety, the description provides enough context for an agent to call it correctly. It mentions the source (highest-liquidity pool), the unique time-series nature, and cost. Without an output schema, it hints at the return format (OHLCV candles) but doesn't detail it, which is acceptable.

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 all parameters (mint, limit, timeframe) are already documented with clear descriptions. The description adds contextual meaning by mentioning OHLCV candles and highest-liquidity pool, but this doesn't go beyond what the schema already implies. Baseline 3 is appropriate when schema covers parameters fully.

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 OHLCV candles for a Solana token from its highest-liquidity pool, which is specific and distinguishes it from siblings by noting it's the only time-series data in the catalog. The verb and resource are clear, and it differentiates from other point-in-time snapshot endpoints.

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 explicitly states this is the only time-series data and every other endpoint is a point-in-time snapshot, which tells the agent when to use this tool (for historical price data) and implies alternatives for snapshots. It also mentions cost, which is a usage consideration. It doesn't name specific sibling tools, but the general distinction 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?

Annotations only cover read-only/idempotent/non-destructive hints. The description adds valuable behavioral disclosures: 'No code execution' addresses a safety concern, and the $0.003 cost plus payment instructions are critical for an agent deciding to call it. It also clarifies no provider account is required, exceeding the annotation baseline.

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 with zero fluff. The core purpose is front-loaded, followed by critical cost and security notes. Every sentence earns its place.

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 fully covers what data is returned, cost, execution safety, and authentication requirements. No gaps remain for an agent 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?

Input schema covers 100% of the parameter with a clear description ('Public Python package name'). The description does not add new parameter-level details like case sensitivity or formatting, so it stays at the baseline for high schema coverage.

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 gives a specific verb ('Inspect') with a resource ('Python package') and enumerates exact data fields (latest release, Python compatibility, dependencies, license, advisories). The Python-specific scope clearly distinguishes it from siblings like get_npmsearch_report and get_package_report.

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 makes the tool's context obvious (Python packages on PyPI) and implies it is the appropriate choice for Python package metadata. It does not explicitly name alternative tools or exclusions, but the python-specific scope and sibling names provide sufficient 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?

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description goes beyond them by disclosing the $0.002 call cost and warning that the result is not a building measurement and does not exempt a home from testing — genuinely useful behavioral context. It stops short of covering auth, rate limits, or response shape.

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, no filler: the first states what the tool returns and where, the second carries the cost and the key interpretation caveat. Front-loaded and fully earning its space.

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 helpfully enumerates the returned data and discloses cost and interpretation limits, which is most of what an agent needs. The remaining gap is parameter handling — nothing explains how lat/lon and place interact or which is preferred.

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 only 33% (only 'place' has a description), and the description adds no guidance on lat/lon/place at all — not even the lat/lon accepted ranges or that place is a geocoded alternative. With low coverage the description should compensate, and it does not.

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 ('Look up') and resource ('area-level geologic radon potential'), then enumerates the returned fields (index, province, geology, EPA screening guidance) and the geographic scope (U.S. location). The sibling list is dominated by unrelated token/crypto endpoints, so no differentiation is needed for an agent to identify this 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?

The description implies usage ('look up ... for a U.S. location') and adds a cost signal plus an interpretive caveat that any home can have high radon and should be tested. However, it never states when to choose this over adjacent geospatial tools like find_superfund_sites or geocode_address, nor what prerequisites (e.g., place-string geocoding) exist.

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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds the cost ($0.005) and outlines the output fields (age, liquidity, buy/sell flow, risk flags), which are useful behavioral details beyond the annotations. No contradiction exists.

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 sentence that front-loads the core purpose ('Brand-new Solana pools'), then efficiently adds cost and data composition. Every word earns its place 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?

For a simple list tool with two optional parameters and no output schema, the description covers what the tool returns (age, liquidity, buy/sell flow, risk flags) and the cost. Annotations handle the safety profile, so nothing essential is missing for an agent to decide when and how to call it.

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 covers 100% of parameters with descriptions for minLiquidity and maxAgeMinutes. The tool description does not add any extra meaning about these parameters beyond what the schema already provides, 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 clearly states the resource (Solana pools) and the distinguishing attribute (brand-new, seconds to minutes old). It also lists the data included (age, liquidity, buy/sell flow, risk flags), making it distinct from siblings like get_trending_pools or get_token_snapshot.

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 'the most perishable data here' implies this is for the newest pools, but it does not explicitly name alternatives or conditions for when not to use it. The usage context is implied rather than stated, so an agent must infer that fresher data is the intended use case.

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?

Annotations already declare readOnly, idempotent, open-world and non-destructive, so the safety profile is covered. The description adds genuinely new behavioral context: reporting years vary by indicator, earliest comparison values are included, source provenance is returned, and each call costs $0.003.

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?

One sentence, front-loaded with the action and result set, with cost appended at the end. The field enumeration is dense but every element maps to real return content, so little is wasted; it could be tightened slightly.

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 carries the burden of describing the return payload, and it does so reasonably well by listing the indicator components and the year-variation caveat. It is nearly complete for a two-parameter read tool whose annotations already cover safety.

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 the country code and the optional goal filter are fully documented in the schema itself. The description adds no extra meaning (e.g., what an omitted goal yields or how multiple goals behave), leaving this at the baseline for a fully-covered 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 opens with a specific verb and resource: 'Return latest reported headline UN SDG indicators for a country,' and enumerates the returned fields (series, values, years, units, comparison values). The resource is clearly distinct from neighbors like get_country_report or get_economy_report, but no sibling is named to sharpen that boundary.

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 guidance on when to choose this tool over get_country_report, get_economy_report, or the other report tools. The only decision-relevant information is the $0.003 cost, which hints at a paid call but doesn't say when the data is worth fetching.

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?

Annotations already establish the safety profile (readOnlyHint, idempotentHint, destructiveHint=false, openWorldHint), so the description only needs to add context beyond that. It does so meaningfully: a per-call cost of $0.002, a stated coverage boundary, and the exact data fields returned, including the percent-of-median normalization context.

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, front-loaded sentences: the first states what is found and returned, the second carries cost and coverage constraints. No filler, though the long field enumeration is slightly list-like.

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 exists, so the description usefully enumerates the return fields (SWE, snow depth, precipitation, temperature, distance, observation date, percent-of-median). Cost and geographic coverage round out what an agent needs. The remaining gap is parameter semantics, which the schema only partially covers.

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 only 20% — just 'place' is documented, while lat, lon, limit, and radius_km carry no description. The description adds no parameter guidance at all (no mention of lat/lon vs. place precedence, no radius behavior), so it fails to compensate for the coverage gap even though the schema at least supplies ranges and defaults.

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+resource (find nearby USDA NRCS SNOTEL stations) and enumerates the returned measurements, which distinguishes it clearly from sibling geospatial tools like get_radon_potential, find_superfund_sites, and geocode_address.

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 gives decision-relevant context — a $0.002 cost and a geographic coverage restriction to the mountainous western U.S. and Alaska — which implicitly tells the agent when this tool is applicable. However, it never states when NOT to use it or which sibling to prefer (e.g., geocode_address to resolve a place first).

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?

Goes well beyond the annotations, which only cover safety hints: it discloses the data source (Yahoo Finance with Nasdaq fallback), latency caveats ('exchange delays can apply'), failure behavior ('unknown symbols are returned as free 404s'), and a concrete billing rate ($0.002). These are exactly the operational traits an agent needs to plan a call.

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?

Effectively two sentences: the first front-loads what is returned, the second packs source, delay, error and cost into one line. No filler and no repetition of the schema.

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 takes on the burden of describing return contents and does so (price, change, ranges, volume, exchange, timestamp); combined with cost, fallback source and 404 behavior, nothing an agent needs to invoke it correctly 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% for the single 'symbol' parameter, so the schema already documents ticker format with examples. The description adds no syntax or format detail beyond that, 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 ('look up') and resource (stock, ETF or index quote) and enumerates the returned fields, which tells the agent this is a live snapshot. It does not, however, differentiate itself from the sibling get_price_history, so the boundary between the two must be inferred.

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 word 'quote' and the listed live fields imply a current-snapshot use case, but there is no explicit when-to-use guidance and no mention of the alternative get_price_history for historical data. Usage is only implied.

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?

Annotations already carry the safety profile (readOnlyHint=true, idempotentHint=true, destructiveHint=false), lowering the bar. The description adds genuine value beyond them by disclosing the $0.005 cost, a real behavioral trait an agent needs before committing, plus the comprehensive data scope. No contradiction with annotations; the return format isn't detailed but there is no output schema to contradict.

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 sentence that front-loads purpose, lists contents, and closes with the cost. Zero filler; every clause earns its place and the cost disclosure is placed at the end where it reads naturally. Ideal density for an agent-facing definition.

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 aggregate snapshot with strong annotations and full schema coverage, the description is nearly complete: it enumerates the data categories returned and the cost. It doesn't describe the response structure or pagination, and with no output schema to lean on that is a minor gap, but the enumerated contents give an agent enough to judge relevance before calling.

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% — the sole parameter mint is already described as a 'Solana mint address (base58)'. The description adds no extra parameter semantics beyond what the schema documents, so the baseline 3 applies. The description implies the mint targets a Solana token but adds no syntax or format detail 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?

States a specific verb+resource: 'Complete Watchdog snapshot for a Solana token in one call' and enumerates the delivered data (price, liquidity, holders, pairs, socials, security flags, scores). The word 'Complete' plus the single-call framing distinguishes it from single-aspect siblings like check_token_price, check_token_holders, and check_token_security, so an agent can tell them apart without opening schemas.

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 'in one call' implies this is the all-in-one aggregation tool versus the granular single-aspect checkers, and the $0.005 cost signals a paid call. However, it never names an alternative or states when NOT to use it (e.g., when only price is needed, prefer check_token_price). The usage context is clear but implicit; no explicit exclusions or alternative routing.

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?

Annotations already declare read-only, idempotent, non-destructive behavior. The description adds the monetary side effect ('Costs $0.01') and the returned metric set, which goes beyond what the annotations and schema convey.

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 short sentences with no filler; every clause carries information about resource, time window, output metrics, and cost. It avoids duplicating schema or annotation data.

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 read-only tool with one optional parameter and full schema coverage, the description is nearly complete. It could clarify the default limit behavior, but that is a minor gap given the schema already documents 'Max 50.'

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 single optional parameter already has a descriptive description ('Max 50'). The tool description adds no parameter-level semantics, so the baseline score 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 names a specific resource (Solana pools), a state ('currently trending'), and the returned metrics (price, liquidity, volume, 24h change), which clearly distinguishes it from token/price-focused siblings like check_token_price and get_price_history.

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 intended use is implied: call it when you need currently trending Solana pools and their market metrics. However, it does not explicitly state when not to use it or name alternatives, so it earns only partial credit.

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?

Beyond the annotations, the description adds meaningful behavioral detail: nothing is hosted, credentials remain with the renter, the call is free, no performance/profitability data can be reported, and trading risks total loss. This contextualizes the read-only, non-destructive, idempotent annotations and sets accurate expectations.

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 three sentences, front-loaded with the core purpose, then adding operational and risk context that is directly relevant to an agent's decision to call it. No sentence is wasted or redundant.

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?

Even without an output schema, the description tells the agent what to expect: rentable bots, prices, durations, and the important caveat that no performance data is reported. For a parameterless list tool, this is fully sufficient for correct selection and invocation.

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 tool has zero parameters and the schema is fully complete, so there is nothing for the description to clarify about inputs. The description instead usefully conveys what the returned list includes: prices and durations.

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 opens with a clear verb and resource: it lists trading bots available to rent, with prices and durations. It distinguishes this from siblings by describing a specific rental marketplace concept that no other sibling tool covers.

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 gives clear context for when to use the tool: when seeking rentable trading bots with fixed periods, pricing, and buy-in expectations. It explains the self-hosted model and that no credentials are sent, which guides trust expectations, though it does not explicitly name alternatives or say when not to use it.

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?

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds valuable context beyond those: it is free (cost/rate implication) and returns the complete result in a single call ('One call reveals the whole shape'), which implies no pagination or partial results. No contradiction with 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?

Two short sentences with zero filler. The resource and scope come first, then the behavioral notes. Every clause adds distinct information: catalog scope, counts, cost, and one-call completeness.

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 zero-parameter, read-only, idempotent list operation, the description plus annotations fully equip an agent to call it. No output schema exists, but the simple return (categories and tags with counts) is adequately implied. Nothing critical 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?

The tool has zero parameters, and schema coverage is trivially 100%. The baseline for 0-param tools is 4, and the description correctly emphasizes that no input is needed ('One call reveals the whole shape'). No parameter-level detail is required.

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 resource ('Every category and tag in the catalog') and adds the outcome (counts, full shape). It is unambiguous about what this tool returns. It does not name sibling tools or contrast itself, but the resource is distinct enough among siblings like list_bot_rentals and list_data_endpoints.

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 context is implied by 'One call reveals the whole shape' — an agent can infer this is the go-to tool for exploring the catalog taxonomy. However, there is no explicit when-to-use guidance, no exclusions, and no mention of alternatives, leaving routing to inference.

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?

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. The description adds meaningful context beyond that: the operation is free, it reflects the live manifest, and it returns the complete set (no filtering). Pagination/result-size behavior is not addressed.

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 what is returned, then the cost and usage cue. 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?

With no output schema, the description compensates by listing the returned fields (description, price, networks, discovery links) and framing the tool as the catalog entry point. Nothing essential to 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?

The tool takes zero parameters, so there is nothing for the description to disambiguate beyond the schema. Baseline for a no-param tool 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 (list) and resource (currently buyable endpoints in the live x402 manifest), and enumerates the payload contents: description, price, payment networks and discovery links. This is distinguishable from siblings like get_endpoint (single) and search_endpoints (filtered query).

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 tells the agent to call this first to see the complete catalog, and notes it is free, giving clear entry-point context. It stops short of naming an alternative (e.g. search_endpoints) or stating when not to use it.

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?

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds valuable context: cost (free), auth requirements (x402, no account/key), and return fields plus the next step, though it omits pagination and 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?

Front-loads purpose and return information, but the middle catalog list is very long. While each domain adds coverage detail, the list could be trimmed without losing the core message.

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 explains exactly what is returned (url, example call, sample response URL, price, description) and the next step (get_endpoint). Annotations cover safety, so the definition is 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 coverage is 50%: the 'query' parameter is documented in the schema, but 'limit' is not. The description's long list of domains indirectly hints at query content but adds no syntax or limit guidance, so it does not compensate for the gap.

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) and resource (Saylor catalog of pay-per-call data endpoints), and distinguishes itself from get_endpoint by noting the follow-up call. The catalog listing makes the scope unambiguous.

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?

Describes its role in the workflow ('then call get_endpoint for payment details'), but does not explicitly compare to sibling list_data_endpoints or list_categories, leaving the agent to infer when to search versus list all.

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?

Annotations already declare readOnly/openWorld/idempotent/non-destructive, so the safety profile is covered. The description adds genuinely new behavioral context beyond that: the returned field list, the caveat that 'source coverage and filing language vary', and a cost disclosure ($0.003). The cost and coverage caveat are the kind of operational detail that isn't in the structured fields.

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 the core action and resource, followed by the return fields and one caveat-plus-cost sentence. Two sentences carry the payload with no filler; the field enumeration is slightly long but earns its place by substituting for a missing output schema.

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 compensates by listing return fields. It also discloses cost and a coverage/language caveat. The main gap is the undocumented 'limit' parameter, but overall an agent has enough to invoke 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 coverage is 67%: 'q' and 'country' are described in the schema, while 'limit' is not. The description adds no parameter-level semantics beyond restating the search intent, so the schema does most of the work. A baseline 3 is appropriate for moderate coverage with no extra description-side detail.

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) and resource (current notices in TED, the EU official procurement database) and enumerates the returned fields (buyer, title, CPV codes, deadline, source link). No sibling tool covers EU procurement, so this is unambiguous and self-contained.

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 names intended consumers ('sales-lead and market-intelligence agents'), which implies context, but gives no explicit when-to-use, when-not-to-use, or alternative tool. Since no sibling overlaps the tender domain, the omission costs little, but the guidance remains implicit rather than stated.

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?

Annotations already declare readOnlyHint=true and idempotentHint=true, so the read-only safety profile is covered. The description adds valuable behavioral context beyond that: the guides are free ('no payment') and the operation returns only metadata, never bodies. These details help an agent set expectations without contradicting 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?

Three short sentences with no fluff. Purpose is front-loaded, followed by cost and return-type constraints. Every sentence earns its place.

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 purpose, cost, and the metadata-only return constraint, and annotations cover safety. However, with no output schema, the description does not detail what metadata fields are returned or how results are paginated. For a tool with 5 optional parameters, this leaves some room for agent uncertainty about the exact response shape.

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 60%, so the schema documents tag, query, and free_only but leaves limit and category underdocumented. The description adds some indirect parameter context by listing topic areas (which map to categories) and mentioning 'Free — no payment' (relating to free_only), but it doesn't systematically explain parameters. This is adequate but not a strong compensation for the coverage gap.

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 states the specific verb 'Search' plus the resource 'Saylor Innovations technical guides' and lists topic areas. It further distinguishes itself from siblings like get_guide by explicitly saying 'Returns metadata only, never bodies.'

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—finding technical guides before retrieving them—and the 'never bodies' line hints at a boundary with get_guide. However, it never explicitly says 'use this to discover guides, then call get_guide for full content' or names any alternative. No clear when-not-to-use guidance is provided.

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?

Annotations declare readOnlyHint, idempotentHint, and destructiveHint false; the description adds context that the tool clones into a disposable environment (non-persistent), runs only declared scripts, and charges $0.25 per attempt. It discloses cost and the limitation on semantic correctness, which go beyond annotations. No contradiction is present.

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 three sentences with no fluff. It front-loads the core action, then states exclusions and cost. Every sentence adds essential information, and the structure is scannable.

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 explains what is returned: 'structured logs plus the resolved commit' and 'the paid HTTP endpoint and x402 instructions.' It covers the core behavior, limitations, and cost. It could mention timeout behavior or error handling, but for an agent to decide whether to invoke, it is sufficiently complete.

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 80%, so the schema already documents most parameters. The description adds that the task is a 'declared test, lint, or build script' and that it runs in a microVM, which reinforces the task and repository semantics. However, it does not clarify ref, timeoutSeconds, or packageManager beyond the schema. The baseline of 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?

The description uses specific verbs and resources: 'Clone a public GitHub repository into a disposable Firecracker microVM, install dependencies, run one declared test, lint, or build script, and return structured logs plus the resolved commit.' It clearly defines the action and output, and distinguishes from sibling tools focused on tokens, wallets, and reports. The clarification 'verifies execution, not semantic correctness' further sharpens 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 explicitly states exclusions: 'private repositories and arbitrary shell commands are not accepted,' and implies the scope of use (public repo verification). It does not name alternative tools, but the sibling set makes the uniqueness obvious. It also mentions the cost and payment endpoint, which guides whether to use it.

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