Web Access Gateway

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

With no annotations, the description carries the full burden. It usefully discloses that the tool costs $0.01 USDC via x402 and returns 5 ranked results, which is meaningful. However, it does not describe rate limits, source freshness, failure modes, or whether any authentication beyond x402 payment is needed.

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 one compact sentence that front-loads the key facts: result count, result type, use cases, and cost. Every phrase adds decision-relevant information with no filler.

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

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

For a one-parameter search tool, the description covers what it returns, how many results, why to use it, and the cost. It lacks detail on output structure and query syntax, but the tool is simple enough that the description is nearly sufficient.

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

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

Schema description coverage is 0%, so the description must compensate for the single query parameter. It never explains what should go in `query`—company name, natural-language prompt, URL—or how the query affects ranking. The schema provides only length constraints.

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 verb and resource: 'Find 5 ranked company and corporate-web results.' It clearly differentiates itself from siblings like web_search and news_search by scoping to companies and corporate web content, and even states representative use cases.

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 intended contexts: market research, vendor discovery, and diligence. However, it does not explicitly mention when to avoid this tool or name alternatives, though the sibling list makes some alternatives inferable.

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

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

With no annotations, the description carries the behavioral burden. It discloses a concrete cost (USDC $0.01 via x402), a fixed result count, and the negative scope (not routing/Maps data). It omits auth requirements, failure modes, and geographic coverage limits, but the paid-search side effect is made explicit.

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: the first fronts the core action and output, the second adds essential exclusion and cost. Every sentence earns its place with no filler.

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

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

For a single-string-parameter tool with no output schema, the description supplies purpose, result shape, relevance cue, exclusion, and cost. The notable gaps are authentication and no-match edge-case behavior, but the core invocation context 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 0%, so the description must carry parameter meaning. It adds that the query should contain 'place terms,' narrowing the free-form string parameter, but it provides no examples, required components (e.g., city, state), or handling of ambiguous location names. This is minimal compensation.

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'), a bounded resource (5 ranked location/address/hours/local-context sources), and a scope condition (place terms in query). The 'Not routing or Maps data' sentence distinguishes it from adjacent mapping tools, though it does not explicitly name sibling search tools it differs from.

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 place-based lookups and explicitly excludes routing/Maps data, giving one clear when-not. It does not name alternatives among siblings (e.g., web_search, news_search) or state when those would be preferred, so guidance is mostly implicit.

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

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

With no annotations provided, the description carries the disclosure burden and does so reasonably: it reveals the time window (at most seven days), the evidence-link output, the non-comprehensive scope, and the $0.50 USDC cost via x402. It does not mention rate limits or authentication, but the cost and scoping details are valuable behavioral 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?

The description is compact—three short sentences—and front-loads the core Massachusetts scope and evidence-backed nature. The caveat about non-comprehensive coverage and cost are both relevant. Slight redundancy ('selected official state and selected municipal') keeps it from being maximally concise.

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 seven parameters, no annotations, and no output schema, the description is insufficiently complete. It provides cost and a time window but omits what the output looks like beyond 'evidence links' and does not explain any parameter semantics or request handling. An agent would struggle to invoke it correctly for a non-trivial request.

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 0% and the description does not explain any of the seven parameters, such as since, topic, lookback_hours, or municipalities. The only tangential hint is 'at most seven days' relating to lookback, but that is not explicit enough to guide parameter usage. The description fails to compensate for the schema's complete lack of parameter documentation.

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

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

The description clearly identifies a specific verb-resource ('snapshot') and a narrowly scoped target: Massachusetts-only selected official state and municipal sources with evidence links. The limitation note ('Not comprehensive coverage...') and cost detail further distinguish it from broader siblings like web_search or geographic_search.

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

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

The description implies when to use it—when Massachusetts government evidence is needed—and communicates the exclusion of comprehensive coverage. However, it does not explicitly state alternatives or provide when-not-to-use conditions, leaving the agent to infer routing based on the state scope and evidence requirement.

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

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

With no annotations provided, the description carries the full burden and does a good job disclosing cost ($0.01 USDC via x402), recency scope, result count, and coverage limitation. It does not detail the output structure or the exact mechanics of x402, but the most important behavioral characteristics are disclosed.

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 short sentences, each adding distinct value: what the tool searches, the limitation, and the cost. It is compact, front-loaded, and free of 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 simple single-parameter search tool with no output schema, the description covers the key invocation facts: time window, result count, cost, and non-comprehensiveness. It leaves out what a result contains (e.g., title, URL, snippet), but this is not essential for deciding whether to call the tool.

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

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

Schema description coverage is 0%, and the description does not explain the query parameter beyond the act of searching. The parameter's purpose is inferable from the tool name and first sentence, but there is no guidance on query phrasing, keyword format, or how to construct effective queries.

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 the specific verb 'Search' and identifies a clear resource: 'recent public web coverage from the last seven days'. It also states the exact return shape ('5 ranked results'), distinguishing it from broader tools like web_search by time window and news focus.

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 about the seven-day window, the five-result limit, and the fact that it is not comprehensive newswire coverage. However, it does not explicitly tell an agent when to choose this tool over sibling tools such as web_search or search_and_read, nor does it name alternatives for broader searches.

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

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

With no annotations, the description carries full burden and discloses important behaviors: it returns exactly 5 ranked sources, is not a live inventory guarantee, and costs $0.01 USDC via x402. This goes beyond merely saying 'search' and helps the agent set expectations about output and cost.

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 crisp sentences, zero fluff, and the most important scoping detail is front-loaded. The cost and caveat are placed after the core behavior, which keeps the main purpose immediately visible.

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 search tool with no output schema, the description covers the key points: result count, source type, caveat about live inventory, and cost. It does not describe the exact return shape, but '5 ranked sources' gives enough expectation for an agent to proceed.

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 compensate. It implies that 'query' should be a product-related search term, but it does not explain query formatting, examples, or whether the query should target brand/model/category. Some meaning is added, but not enough to fully document 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 specifies a concrete action ('Find') and a clear resource ('5 ranked public-web product, price, specification, and review sources'). This differentiates it from siblings like news_search, company_search, and generic web_search by scoping to product-related research.

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 implies when to use this tool: when the agent needs product, price, specification, or review information from the public web. It does not explicitly name alternative tools or provide exclusions, but the product-focused scope gives enough contextual signal for 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It usefully reveals that content is 'clean bounded', that a SHA-256 hash accompanies it, and that there is a $0.003 USDC cost. However, 'bounded' and 'clean' are left vague, and failure modes, redirects, size limits, or return structure are 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?

The description is two sentences with no filler. The first sentence front-loads the core action and result, and the second adds the crucial cost detail. Every word contributes value, and the structure is easy to scan.

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

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

The tool is simple with one parameter and no output schema, and the description covers the essential aspects: what the tool reads, the public constraint, the output characteristics, and the cost. However, 'bounded' and 'clean' are undefined, no output schema exists to clarify the response shape, and there is no mention of error handling or size limitations, leaving notable gaps for an agent.

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 provides only the field name, format, and max length, with 0% parameter description coverage. The description adds a meaningful constraint by stating the URL must be 'public', which is a semantic requirement not present in the schema. Still, it does not clarify protocol expectations, URL normalization, or other edge cases, so the compensation is partial.

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

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

The description clearly states the tool's action ('Read'), the resource ('one public URL'), and the outcome ('return clean bounded page content with a SHA-256 content hash'). This directly distinguishes it from the sibling search tools, which are for discovery rather than direct URL retrieval, and the single-URL focus differentiates it from search_and_read.

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 tool should be used when one has a specific public URL to read, but it offers no explicit guidance about when to prefer this tool over alternatives like search_and_read or the various search tools. There are no exclusions, prerequisites, or comparison cues, leaving tool selection 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?

No annotations are present, so the description carries the behavioral burden. It usefully discloses the cost ('$0.025 USDC via x402'), the fixed result count, clean page content, and content hashes. It does not cover failure modes or rate limits, but the key behavioral traits are 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 two short sentences, with the core functionality front-loaded and the cost mentioned second. No filler or redundant restatement of the tool name appears.

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 simple single-parameter schema and no output schema, the description adequately explains what the agent will receive: 5 ranked results with clean page content and hashes, plus the cost. It could add a little more about result ordering or failure behavior, but the essential context 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?

With 0% schema description coverage, the description should compensate for parameter documentation, but it adds no detail about the 'query' parameter beyond the schema's name, type, and length. The parameter name is fairly self-explanatory, yet the description does not clarify accepted formats or intended usage.

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

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

The description states a specific action ('Search the current web') and clear outputs ('5 ranked results with clean page content and content hashes'). It conveys the tool's combined search and read purpose, though it does not explicitly contrast itself with sibling tools like web_search or read_url.

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 guidance about when to prefer this tool over web_search, read_url, or other siblings. The description implies a web search use case but provides no conditions, exclusions, or alternatives.

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

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

With no annotations, the description carries the transparency burden. It discloses meaningful behavioral facts beyond the schema: the call costs $0.01 USDC via x402, returns exactly 5 results, ranks them, and returns compact source excerpts. It does not cover auth, rate limits, or error cases, but the cost disclosure is notably useful.

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 with no redundancy. The core output contract is front-loaded, and the cost is stated clearly. 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?

For a one-parameter web search tool, the description covers the main things an agent needs: input is a query, output is 5 ranked web results with excerpts, and there is a monetary cost. The absence of an output schema is partially mitigated by mentioning the fixed JSON schema, but exact fields are still unspecified.

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

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

There is only one required parameter, 'query', which is already documented in the schema with type, min, max, and required status. The description does not add detail about query formulation, language support, or how results are ranked. This is acceptable for a self-explanatory parameter but does not go 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 ('Return') and resource ('web results'), and adds useful specificity: 5 results, ranked, current, with compact excerpts in a fixed JSON schema. It does not explicitly differentiate from sibling tools like news_search or product_search, but the general 'web results' scope is clear enough.

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 no guidance on when to choose web_search over alternatives such as news_search, company_search, or geographic_search. There is no mention of use cases, exclusions, or conditions that would favor a sibling tool.

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