PyScrappy

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

No annotations are provided, so the description carries the full burden. It reveals that rates are 'live' and provides the exact return structure, which is useful. However, it does not disclose potential pitfalls such as invalid currency codes, handling of unknown target codes, rate staleness, or external API dependencies. For an unannotated tool, this is a notable gap.

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 states the core action concisely; the second handily illustrates the return structure with a concrete example. No redundant wording, front-loaded purpose.

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

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

For a simple three-parameter, all-optional tool, the description plus rich schema fully cover how to call it and what to expect. The return example doubles as a de facto output schema. The only missing piece is error behavior, but that is not strictly required for a straightforward conversion 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 detailed descriptions for 'to', 'base', and 'amount', including examples and defaults. The description adds no extra parameter meaning beyond the schema, 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 states a specific verb ('Fetch live exchange rates and convert') and resource (currencies), clearly distinguishing it from all sibling tools, which are spam/scrapers/search tools. No 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?

There is no direct sibling for currency conversion, so no alternative routing is needed. The description clearly implies this is the tool for any currency-conversion need. It lacks explicit 'when not to use' guidance, but the scope is obvious from the unique functionality.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It explicitly notes that the tool is read-only with no side effects, requires a network connection, and handles not-found cases by returning an empty or not-found response instead of raising an error. It also describes the structure of the return value. This is substantial coverage for a simple lookup tool, though it could mention rate limits or authentication if applicable.

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 paragraphs with no fluff. The purpose is front-loaded in the first sentence, and the second paragraph adds essential behavioral details (network, error handling, return structure). Every sentence earns its place, and the length is appropriate for the tool's complexity.

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

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

The tool is simple with one parameter and a structured output schema. The description covers the purpose, behavioral notes, and a summary of the return value. Given that an output schema exists, the description does not need to detail every field. It is complete enough for an agent to understand what the tool does and how 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?

The input schema has 100% coverage and already fully describes the 'word' parameter, including its type, example, required status, and constraints (single words only). The tool description does not add any additional meaning beyond what the schema already provides, so it meets the baseline of 3 for a high-coverage schema. No further elaboration is needed.

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

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

The description clearly states the action (look up an English word) and the resource (dictionary entry), listing specific content: definitions, parts of speech, and example sentences. It is distinct from the sibling tools, which are all web scraping or search tools, so there is no ambiguity about what this tool does.

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 does not explicitly state when to use this tool versus alternatives, though the sibling list makes it obvious it is for dictionary lookups. It does provide a constraint in the schema parameter (single words only, not phrases or non-English terms), but there is no explicit routing guidance such as 'use this when you need a definition rather than scraping a page.' The intended use is implied 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?

With no annotations, the description carries the behavioral burden. It discloses that the tool makes a network call, requires internet, reads no local state, returns top coins when query is omitted, and returns an empty list rather than throwing on no matches. This meaningfully sets expectations, though it could also mention failure behavior on network errors.

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 focused paragraphs with no filler. The return shape is front-loaded, the network/read-only behavior is stated next, and edge-case behavior is briefly covered. 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 read-only lookup tool with three well-documented optional parameters and an output schema, the description is complete. It covers result shape, defaults, edge cases, and operational context without needing to restate schema details.

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 coverage is 100%, so the baseline is 3. The description adds value by clarifying that query is a comma-separated id list, that omission means top coins by market cap, and that prices are expressed in vs_currency. It complements rather than restates 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 ('fetch') with a clear resource ('live cryptocurrency market data') and precisely enumerates the returned coin fields. It is immediately distinct from sibling tools like convert_currency or search_news, even without naming 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 establishes clear context: use this when you need current cryptocurrency prices, top coins by market cap, or coin lookups by id. It does not explicitly name alternatives or state when not to use it, but the intended domain is unmistakable from the 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?

With no annotations, the description fully discloses behavior: it scrapes live, requires internet, reflects current listings, depends on location/hours, returns a structured list grouped by category, and returns empty results on error scenarios (invalid URL, unavailable, empty menu). This covers the key behavioral traits an agent would need to know, including error handling, without any contradictions.

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

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

The description is three sentences, each earning its place: purpose, behavior (scraping, network, variability), and error handling. It's front-loaded with the primary action and efficiently conveys all necessary details without fluff. No redundant information.

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

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

Given the presence of an output schema and a well-documented parameter, the description is complete for an agent to invoke the tool correctly. It explains network requirements, result structure, error behavior, and data dependencies. No missing critical information is apparent.

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 covers the parameter 100%, already specifying the URL format and source (from search_ubereats). The description's reference to 'store URL' adds no new meaning beyond the schema. Since schema coverage is high, the baseline is 3; the description does not compensate with additional param details, so 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 clearly states the tool fetches a live menu from a store URL and returns item names, descriptions, and prices. It uses a specific verb ('fetch') and resource ('Uber Eats restaurant's live menu'), distinguishing it from generic scraping tools like scrape_url and search tools like search_ubereats. No 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 usage by referencing a 'store URL' and the need to scrape over the network, but it does not explicitly state when to use this tool versus alternatives. The input schema clarifies the URL comes from search_ubereats results, which provides context, but the description itself lacks explicit when/when-not guidance. Slightly below excellent because it doesn't name the alternative or condition explicitly.

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, and it does well by stating that each call makes a live network request, depends on an external provider, needs internet access, and returns an empty/error result rather than raising. It could also mention latency or rate limits, but the core runtime behavior is transparent.

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

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

The description is compact and front-loaded: first sentence states the action and return contract, second sentence covers runtime behavior and error handling. Every sentence earns its place, with no repetition of the title or 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 an output schema, the description is nearly complete: it covers invocation, return format, live-network behavior, internet dependency, and failure handling. The only notable gap is explicit guidance on when to prefer this over sibling tools, which is already captured under usage guidelines.

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 parameter description already explains the expected string format, optional region/country disambiguation, and that it is required. The tool description adds only general context about 'named place' and the resolved place name in the response, so it does not significantly extend the schema's meaning.

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 and resource ('Fetch the current weather conditions for a named place') and goes further to enumerate the exact return keys and their types. This clearly distinguishes it from the sibling search and scraper tools, even without an explicit comparison.

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 core use case clear: retrieve current weather for a named location. However, it provides no explicit guidance about when to choose this tool over siblings or when not to use it, leaving tool-selection routing mostly 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly states that no network or browser access is performed, no state is changed, and that the tool reads an in-process registry. It also covers the empty-list edge case and plugin self-registration behavior.

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

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

The description is well-structured with a purpose statement, behavioral details, return format, and usage guidelines. It is slightly redundant (e.g., empty list behavior appears twice, and the connection to scrape_with is stated more than once), but remains focused and front-loaded.

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

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

Given the tool's simplicity (zero parameters, output schema available), the description is fully complete. It explains what values are returned, how they should be used, what happens when no scrapers exist, and what side effects (none) to expect.

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, so there is no parameter semantics to add beyond the schema. The description still confirms the absence of parameters by describing the tool as a simple registry listing, and it clearly explains what the returned values mean.

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 and resource ('List every scraper registered with this server') and states the results are for use with scrape_with. This clearly distinguishes it from sibling scraping tools like scrape_url and scrape_with, which actually perform scrapes.

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 instructs the agent to call this first to discover valid scraper names and then pass a returned name to scrape_with. It also gives a concrete verification use case (confirming a plugin registered), leaving no doubt about when and how to use the 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?

With no annotations, the description carries the full burden and meets it well: it discloses network reads, that nothing is written or cached, the external API key requirement, the error format for a missing key, and the empty-list behavior on no matches. This is substantial transparency for a read-only external API 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?

Two tightly packed paragraphs, with the core lookup behavior and response shape front-loaded, followed by essential operational details (network, key, error behavior). No filler or redundant restating of the title.

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?

Complete for a network-backed lookup tool: it covers both query modes, return shapes, API key setup, failure behavior for missing key and no matches, and explicitly notes it is non-destructive. The presence of an output schema further covers return details, and the description adds what schema/annotations cannot.

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 baseline is 3. The description reinforces that 'query' can be a title or an 'tt' IMDB id and that the response shape differs by input, but the schema already documents these details. The description adds no meaning beyond the schema for the parameters themselves.

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 ('movie and TV data from IMDB via the OMDb API'), and clarifies the return type and shape. It stands apart from sibling scraping/search tools by describing an HTTP API lookup rather than browser scraping, so an agent can distinguish it without inspecting others.

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

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

Provides clear context for when to use the tool and how to choose between title search and IMDB-id lookup, including behavior on no matches and missing API key. It does not explicitly name alternatives or state when not to use it, but the 'no browser is needed' line implies a contrast with scraping 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?

With no annotations provided, the description carries the full burden. It discloses that fetches are live, results are not cached, and explains return behavior (one article vs. up to max_articles, empty list on failure). It also mentions network usage and potential non-return scenarios. This is comprehensive for a scraping 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?

The description is two well-structured sentences. The first sentence states the primary purpose and output format, front-loading the core action. The second sentence provides usage constraints and behavioral details. There is no redundancy or fluff; 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 all essential aspects for correct invocation: input options, exclusivity constraint, return behavior, and failure handling. It accounts for network usage and caching. The presence of an output schema covers return format specifics. No critical information is missing for an agent to use this tool correctly.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds crucial semantic value beyond the schema: it enforces mutual exclusivity of feed_url, site_url, and article_url, and clarifies that max_articles only applies to feed_url and site_url. These are not apparent from the schema alone, so the description elevates the score to 4.

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

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

The description clearly states the tool fetches news articles from RSS/Atom feeds, news sites, or single articles, and returns a list of article dicts. It specifies the verb, resource, and output format, and differentiates from generic siblings like scrape_url by focusing on news content and feed auto-discovery.

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

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

The description provides clear usage instructions: 'Provide exactly one of feed_url, site_url, or article_url.' It explains the behavior of each mode and the max_articles limit. It doesn't explicitly mention when not to use this tool versus alternatives, but the purpose is unambiguous enough that an agent can select it for news-related scraping tasks.

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

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

No annotations are provided, so the description carries the full burden. It discloses the external data source, the dict return type, and how the output varies by mode. However, it does not mention whether the operation is read-only, how errors or invalid symbols are handled, or any rate-limit/network caveats typical of a scraping 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?

The description is compact, well-structured, and front-loaded with the core purpose. The mode-by-mode output list is dense but easy to scan, and every sentence contributes useful information without drifting into fluff.

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

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

Given the tool's modest complexity, the description covers the essential call contract: parameters, mode behavior, and return structure. It lacks only explicit guidance on edge cases like invalid symbols, repeated calls, and rate limits, but the output schema and full parameter coverage make the definition sufficient for 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?

Schema description coverage is 100%, so the baseline is 3. The description adds value by tying each mode to a concrete output shape and clarifying that period and interval are history-specific, which complements the schema's parameter descriptions rather than merely repeating them.

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: 'Fetch stock market data from Yahoo Finance and return it as a dict.' This clearly distinguishes the tool from crypto, weather, and search siblings. The mode-dependent return shapes add precision, making the tool's 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 clearly communicates it is for stock market data from Yahoo Finance, which implies when to use it. However, it does not explicitly name alternatives or state 'use get_crypto for crypto' or 'do not use for non-stock assets', so it falls short of 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?

With no annotations, the description carries the full burden and does it thoroughly: network fetch, no storage/mutation, static vs JS rendering behavior, the error hint, and the exact empty-result shape (data [], count 0, errors) instead of raising. This is exemplary disclosure.

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

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

The description is dense but efficient: first sentence states purpose and output shape, and the rest covers behavior, failure mode, and the JS-rendering option. Every sentence earns its place; 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?

Given the output schema exists and the input schema is fully documented, the description supplies the missing behavioral context: network effects, JS caveat, dependency requirement, and failure semantics. Nothing needed to invoke the tool 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%, and the schema already documents url, max_pages, render_js, and selectors with defaults and examples. The tool description reinforces the render_js behavior but adds little meaning beyond the schema, matching the baseline for high 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 opens with 'Scrape any HTTP(S) URL and return...' which names a specific verb, a clear resource, and the general-purpose scope. This distinguishes it from the specialized scrapers in the sibling list without requiring a tool-by-tool comparison.

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 gives concrete guidance for the main decision point: use the default static request unless the page is client-side rendered, in which case set render_js=true. It does not explicitly call out sibling tools as alternatives or state when not to use this tool, so it stops short of a 5.

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

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 well: it discloses that a live network request is made, that the query is resolved to the best-matching article, and that empty results are returned when no match exists. It could add error-handling or rate-limit context, but the core behavioral traits are transparent.

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

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

The description is well-structured and front-loaded: purpose first, then network behavior, then mode semantics, then edge-case behavior. Every sentence adds necessary information and there is 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?

Given the output schema exists and the parameter schema covers both parameters, the description is largely complete. It explains the mode-dependent return shapes and empty-result behavior. It could mention network failure or default-mode behavior, but overall it gives an agent enough to invoke the tool correctly.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds useful meaning by explaining each mode's return shape and the no-match behavior. However, it does not clarify the default for `mode`, and the schema itself is internally inconsistent (default 'full' vs 'No default (required)'), so the description misses a chance to resolve that ambiguity.

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: 'Fetch a Wikipedia article by title or search term and return its text content.' This clearly distinguishes it from sibling scrapers like scrape_url or scrape_stock, and the mode details further specify what 'text content' means.

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 intended use clear: retrieving Wikipedia article content by title or search term. It does not explicitly name alternatives or state when not to use it, but the Wikipedia-specific scope is unambiguous enough that an agent can select it correctly among the listed 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?

With no annotations provided, the description carries the full burden and does an excellent job. It discloses that side effects depend on the target scraper, mentions network/browser/auth possibilities, distinguishes 'not found' errors from empty results, and states that returns are scraper-specific.

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

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

The description is concise yet complete, with the core purpose front-loaded and supporting details about lookup, return shape, side effects, and error behavior presented in a logical order. 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 generic dispatch tool with no annotations, the description covers all critical operational aspects: how it dispatches, what it returns, what side effects may occur, and how errors differ from empty results. The output schema handles return-value details, so nothing essential 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 schema already provides 100% coverage of both parameters, including examples and forwarding semantics. The description reinforces that args are forwarded to the scraper, but it does not add meaning beyond what the schema already contains, so the high-coverage 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 that this tool runs any registered scraper by name and returns its raw output, using a specific verb and resource. It also distinguishes itself as the generic dispatch entry point for scrapers without a dedicated tool, setting it apart from sibling tools like scrape_wikipedia and scrape_stock.

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 to use this for scrapers that lack a dedicated tool, especially third-party plugins. It does not name the dedicated alternatives individually, but the sibling list provides those options and the intended scoping is clear.

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

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

With no annotations provided, the description carries the full transparency burden and does well: it discloses outbound network dependency, live-data variability between calls, result ordering, capping by max_results, and the empty-list behavior. It does not mention potential rate limits or blocking, but the provided behavioral caveats are substantial and 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?

The description is well-structured and front-loaded, with the core purpose in the first sentence followed by behavioral details. There is mild redundancy between 'Search Zomato...' and 'Scrapes Zomato's public restaurant listings...', but overall every sentence earns its place and no extraneous content 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 presence of an output schema and the tool's straightforward parameter set, the description is complete enough: it covers return shape, ordering, capping, unknown-city behavior, no-match behavior, and network requirements. Minor gaps like pagination or error modes are not critical for this tool's complexity.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds minimal meaning beyond the schema: it restates the query filter as 'cuisine or name term' and says results are 'capped at max_results', but most parameter meaning (examples, defaults, types) already lives in the structured 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 first sentence states a specific verb and resource: 'Search Zomato for restaurants in a city and return a list of restaurant records.' This clearly differentiates the tool from siblings like scrape_wikipedia, scrape_stock, and search_ubereats by naming the exact platform, data type, and action.

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 need Zomato restaurant listings for a city, optionally filtered by cuisine/name. However, it never explicitly contrasts this tool with alternatives like scrape_url or search_ubereats, nor states when not to use it. The usage context is clear but the decision guidance is left 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?

With no annotations present, the description carries the full burden of behavioral disclosure. It explicitly states this is a live network scrape, requires no login or API key, has no side effects beyond HTTP requests, may be affected by region, availability, and anti-bot throttling, and details the exact return shape including empty string/None treatment and empty list on failure. This is comprehensive and exceeds typical descriptions.

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 in the first sentence, then adds behavioral context and return format details in a logical, dense second paragraph. Every sentence contributes value—no filler, no repetition of schema fields. It is suitably concise for the complexity of the tool.

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

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

For a tool with no annotations and an output schema (which already captures return structure), the description covers all essential operational context: authentication, side effects, variability, throttling, empty results, and failure modes. It leaves no critical gap 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?

Schema description coverage is 100%, so the input schema already fully documents both parameters. The description adds no additional parameter-level meaning; it only says 'for a query' in passing. Baseline of 3 is appropriate because the schema handles parameter documentation, and the description does not need to compensate.

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: 'Scrape Amazon search results for a query' and clearly states the return payload (list of products with title, price, rating, image URL). This distinguishes it from siblings like search_newegg or search_books by explicitly targeting Amazon, and from generic scrape tools by specifying the query-driven search use case.

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

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

The description provides clear context that this tool is for Amazon search listings, including live network behavior and no authentication. It does not explicitly name alternative tools or exclusions, but the specificity of 'Amazon search results' makes the usage context obvious. It falls short of a 5 because it never explicitly says 'use this instead of X' or 'do not use when Y'.

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 fully carries the behavioral disclosure burden. It states that the tool queries the network, requires no browser/auth, returns empty lists for no matches, omits missing fields or uses None, and is read-only with no local file/state modification. This is unusually complete for a search 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?

The description is front-loaded with the action, then provides targeted details about network behavior, return structure, empty results, and read-only semantics. Every sentence adds value for an agent deciding whether and how to call the tool; there is 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 two-parameter search tool with an output schema, the description covers the important invocation context: what it searches, where it queries, what it returns, how missing fields are handled, the empty-list case, and that it is non-destructive. Nothing essential is missing for correct selection and use.

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 baseline is 3. The description repeats that the query can be by title, author, or free text, but adds no parameter-specific meaning beyond what the schema already documents for 'query' and 'max_results'. It neither compensates for a gap nor introduces new confusion.

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-resource pair: 'Search books by title, author, or free text via the Open Library search API and return a list of matching book records.' It names the exact upstream source (Open Library) and the book domain, which clearly differentiates it from sibling tools like search_images, search_youtube, or scrape_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?

The description gives clear context: it is a network-based search for book records, requires no browser or authentication, and is read-only. It does not explicitly name alternative tools or state when not to use it, but the domain specificity and the large set of clearly unrelated siblings make the intended usage obvious.

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 behavioral burden and does it well: it discloses network access, GitHub API rate-limit susceptibility, return format with field names and types, ordering by the sort argument, and empty-list behavior. It stops short of covering error handling or authentication requirements, but it is substantially transparent.

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

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

The description is compact and front-loaded: the first sentence states the core purpose, and the second adds behavioral and result details without filler. Every sentence earns its place, and there is no redundant or vague 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?

For a public search tool with fully documented parameters, the description covers the main call semantics, result shape, no-match case, and network/rate-limit caveats. It does not enumerate alternatives or error behavior, but those are secondary for a no-annotation search endpoint.

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 input schema already fully documents query, sort, and max_results with defaults and examples. The description adds only that results are ordered per the sort argument, which mostly restates 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 opens with a specific action and resource: 'Search GitHub for public repositories and return a list of repository records.' This clearly distinguishes the tool from sibling search tools like search_images or search_youtube by naming the GitHub domain and describing the repository record output.

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 GitHub-specific scope makes the intended use clear, and the description explicitly notes it queries the GitHub search API, so an agent can infer when to choose this tool. It does not explicitly name alternatives or exclusion conditions, but no misleading usage signals are present.

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

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

With no annotations, the description carries the full burden and does a good job: it discloses that the tool is a read-only network query ('makes no local changes', 'queries the public Hacker News search index'), and it specifies the empty-list behavior for empty queries and no matches. It omits potential rate limits or error behavior, but those are not critical for a simple search 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?

The description is three concise sentences with no fluff. It front-loads the purpose and return shape, then gives network/read-only behavior, then the empty-result edge case. Every sentence contributes distinct, 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 low-complexity tool with full parameter schema coverage and an output schema, the description is complete enough: it explains result field names, clarifies network access and safety, and defines edge behavior. An agent can confidently select and invoke this tool without additional context.

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?

All four parameters are already fully documented in the input schema with descriptions, defaults, and examples (100% schema coverage), so the baseline is 3. The description adds no additional meaning for by, tags, query, or max_results beyond what the schema already states.

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: 'Search Hacker News stories and return a list of matching story dicts'. This clearly distinguishes it from sibling search tools like search_images, search_github, and search_books by naming the exact content domain and result format.

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 establishes clear usage context by stating this searches the public Hacker News index over the network. It does not explicitly name alternatives or exclusions, but the tool's resource is unique among siblings, so a 'when to use' statement is mostly implicit in the 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?

With no annotations, the description carries the full burden and does it well: it discloses live scraping at call time, network dependency, per-country/per-language pricing and availability, and the empty-list behavior for no matches. This goes well beyond what the input schema or tool name 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?

The description is three concise sentences: purpose and return shape, live-scraping behavior with country/language context, and empty-result behavior. Every sentence adds distinct value, and the most important identifying information is front-loaded.

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

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

For a 4-parameter tool with no annotations, the description covers the key operational caveats: live network scraping, per-country pricing, and empty results. An output schema exists and the return fields are named, so return-value details are adequately covered. Minor gaps remain around failure modes (e.g., network errors or blocked scraping), preventing a perfect score.

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 schema already documents query, country, lang, and max_results in detail. The description adds useful context about country-specific live pricing, but it does not need to repeat parameter-level details; 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 opens with a specific verb and resource: 'Search IKEA's online catalog for furniture and home products.' It also names the return shape (list of product dicts with fields), which clearly distinguishes it from sibling tools like search_amazon or scrape_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?

The description clearly establishes when this tool is appropriate: for IKEA-specific product searches with live store data. It does not explicitly name alternatives or exclusion conditions, but the context is clear enough that an agent can route to it versus the other catalog and search tools.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It thoroughly explains the return format (list of dicts with specific keys), per-engine variations (Google fills only url, title, source_page), handling of missing fields (empty strings or null), and result ordering. No contradictions; all behavioral traits 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 efficient and well-structured: a one-sentence purpose followed by a focused paragraph on output format. Every sentence adds necessary detail, and the most important information (what the tool does and what it returns) is front-loaded.

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

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

Given the tool's complexity (multiple engines, varied output fields), the description is complete. It explains the result object keys, engine-specific behavior, missing-field conventions, and ordering, covering everything an agent needs to invoke and interpret results correctly. An output schema is present, so return-value details are not 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%, so the baseline is 3. The description does not add significant meaning beyond the schema for parameters; it mostly restates what the schema already documents (e.g., query is a string, engine defaults to bing, max_images caps results). No additional parameter detail is provided.

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 web for images') and a clear resource (images), and differentiates from sibling tools by focusing exclusively on image results with URLs and metadata. It is unambiguous and not a tautology.

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 conveys that this tool is for image searches, which implies when to use it (when images are needed) but does not explicitly mention alternatives or when not to use it. It provides clear context but lacks explicit exclusionary guidance.

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

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

With no annotations, the description carries the full burden and does so thoroughly: it discloses live network scraping, no-login operation, call-time variability, the expected return structure ('list of dicts... typically with keys'), and empty-list behavior. This is far more transparent than most tool descriptions.

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 focused sentences with the core purpose front-loaded. Every sentence earns its place by adding behavioral or outcome detail without repeating schema content or padding.

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

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

Given no annotations, the description still supplies the key context an agent needs: operation type, network dependence, authentication requirement, result format, and empty-result behavior. The output schema is also marked as present, and pagination is handled by the schema. The description is complete enough 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?

Schema coverage is 100%, so the input schema already documents query, location, and max_pages with examples and defaults. The prose 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?

States an unambiguous action ('Search LinkedIn public job postings') and a concrete outcome ('return a list of matched jobs'). This clearly differentiates from generic scrapers and sibling search tools by naming the specific resource and domain.

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

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

Provides clear context: the tool is for public LinkedIn job postings, works over the network with no login required, and returns live results. It does not explicitly name alternatives or exclusion conditions, but the domain-specific scope leaves little ambiguity about when 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?

With no annotations, the description takes the full burden and succeeds: it discloses that this is a live network scrape, requires outbound internet, is read-only with no side effects beyond HTTP, and returns an empty list on no matches or parse failure. That goes beyond the schema and 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 focused sentences: the first defines purpose and return shape, the second covers operational behavior and edge cases. No filler, redundant detail, or wasted 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?

Given a 2-parameter tool with full schema coverage and an output schema, the description covers purpose, return fields, network requirement, read-only nature, and empty-list behavior. The only minor gaps are operational details like rate limits or anti-bot behavior, which are not critical for calling the tool correctly.

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

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

Schema description coverage is 100%, so the schema already documents both 'query' and 'max_pages' with examples and effects. The tool description adds no additional parameter semantics beyond what the schema provides, earning the baseline score.

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 a specific verb ('Search'), a specific resource ('Newegg'), and a domain ('electronics and computer hardware'), plus the exact shape of the returned product dicts. This clearly distinguishes it from sibling scrapers/search tools by site and product 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 implies usage: when you need product data from Newegg's search results. It does not explicitly state when to choose this over alternatives like search_amazon or scrape_url, nor does it mention exclusions. The name and siblings make the context reasonably inferable, but explicit guidance is absent.

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 thoroughly discloses that the tool launches a headless browser, requires an extra dependency, is slower/heavier, shows live public SoundCloud results, needs no login/API key, and returns an empty list for no matches.

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 appropriately sized and efficiently structured. The purpose and return format are front-loaded, then caveats follow in a second paragraph. Every sentence contributes meaningful information with no fluff.

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

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

For a tool with no annotations and no shown output schema, the description is complete: it explains prerequisites, behavior, performance trade-offs authentication requirements, and empty-result behavior. An agent has enough information to decide whether to call it and what to expect.

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 both parameters with examples and defaults. The description adds little about parameter behavior beyond the schema's existing coverage, 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?

States a specific verb and resource ('Search SoundCloud for tracks') and describes the exact return shape as track dicts with known keys. It also distinguishes itself from HTTP-based search tools by noting its browser-backend rendering, so an agent can tell it apart from sibling tools.

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

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

Provides clear context for when to use this tool: it is slower and heavier than HTTP-based search toolsais and requires the pyscrappy[browser] extra, implying it should be chosen only when SoundCloud's JavaScript-rendered results are required. It does not explicitly name alternatives or state hard exclusions, but the guidance is strong.

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

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

With no annotations provided, the description carries the full behavioral burden and does so well. It discloses that the tool makes a live network call, requires no API key, caps results at max_results, returns non-fatal errors in an envelope, and handles unrecognized cities with empty data and count 0. This is rich, accurate 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 two concise paragraphs with the purpose front-loaded in the first sentence. Every sentence adds value: output shape, network behavior, cap, error envelope, and edge cases. There is no fluff 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?

The description covers input, output, error handling, edge cases, and the follow-on tool (get_ubereats_menu). It even explains the ScrapeToolResult envelope's fields. For a 2-parameter search tool, this is comprehensive and leaves no significant gap for an agent making the call.

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 baseline is 3. The description reinforces that max_results caps the list and mentions edge-case behavior for the city parameter, but it does not materially add beyond the schema's parameter descriptions. No parameter descriptions are missing, but the description's contribution is marginal.

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

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

The description states a specific verb and resource: 'Search Uber Eats for restaurants delivering in a given city'. It also describes the return envelope (ScrapeToolResult with restaurant objects), which clearly differentiates it from siblings like scrape_zomato or search_amazon. The mention of get_ubereats_menu as the consumer of the returned URLs further clarifies its unique role.

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 its usage context clear: it is for discovering Uber Eats restaurants in a city and provides the store URL for a downstream get_ubereats_menu call. It does not explicitly contrast with alternative scraping/search tools or state when not to use it, but the Uber Eats specificity and the downstream tool reference give enough guidance for the agent.

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

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

With no annotations provided, the description carries full burden. It discloses live network search, variability between calls, read-only nature with no side effects, and empty-list behavior on no matches. This is strong behavioral context, though it omits potential rate limits or auth requirements.

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

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

Two sentences with the main purpose front-loaded. The second sentence packs useful behavioral and return details without filler. The enumeration of return fields is slightly verbose but valuable, keeping the overall structure efficient.

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

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

For a two-parameter search tool, the description covers purpose, network behavior, read-only status, return shape, and empty-case handling. With an output schema present, the return-field listing is redundant but not problematic. Missing edge cases like errors or pagination are 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?

Schema description coverage is 100%, so the schema already documents query and max_results with types, examples, and defaults. The description adds no parameter-specific meaning beyond what the schema provides, so baseline 3 applies.

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

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

States a specific verb and resource: 'Search YouTube for videos matching a query and return a list of matching videos with their metadata.' This clearly distinguishes it from sibling tools like search_images or search_hackernews by naming YouTube 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?

No explicit when-to-use or when-not-to-use guidance, and no mention of alternative tools. The purpose is implied by the name and description, but an agent must infer when to select this over other search tools. The live-network and read-only context hints at behavior, not usage selection.

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