CrawlForge MCP Server

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds substantial behavioral context: it plans searches, fetches and filters pages, returns prose or structured answers, has hard limits, confirms before pro execution, degrades gracefully without LLM keys, and costs credits scaling with maxUrls. No contradiction with annotations.

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

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

Every sentence contributes: use case, mechanism, model variant, hard limits, pro confirmation, degraded mode, exclusions, cost, and example. The key trigger is front-loaded and the density is justified by 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?

For a 6-parameter autonomous agent tool with no output schema, the description provides enough context to call it correctly: expected return shape, limits, cost, failure behavior, alternatives, and a runnable example. Nothing essential seems 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 schema carries the baseline parameter documentation. The description adds meaning beyond the schema by explaining the maxUrls cost scaling, the model behavior difference, and a concrete invocation example that maps prompt and maxUrls together.

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

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

States a specific verb and resource: an autonomous agent that researches, navigates, and synthesizes an answer from the web. It explicitly distinguishes itself from siblings by saying it is not for a URL you already have (scrape) or a question one search answers (search_web).

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?

Opens with a direct when-to-use condition ('when you need an autonomous agent... no URLs required') and provides explicit exclusions for scrape and search_web. It also adds usage nuance for model selection, maxUrls costing, and the confirmation requirement before pro runs.

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

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

Annotations already provide readOnly/idempotent/non-destructive hints, so the description's burden is lower. It adds useful behavioral context beyond annotations: the 3-credit cost and the constraint that it accepts inline text rather than a URL. It does not describe the output structure, but the listed metrics partially imply what will be returned.

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 every sentence earns its place: when to use, what it analyzes, input format, exclusion, cost, and a concrete usage example. It is front-loaded with the core purpose and ends with the example, which is ideal.

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 read-only, idempotent analysis tool, the description covers the key operational details: input expectations, cost, example invocation, and exclusionary context. The main gap is the lack of an explicit return-shape statement, but the absence of an output schema is partially mitigated by the listed analysis metrics.

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

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

Schema coverage is 100%, with text described as 'The text content to analyze'. The description adds concrete meaning by showing example option keys (extractTopics, includeSentiment) and clarifying that text is the raw content, not a URL. This compensates for the empty options object in the schema.

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

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

The description clearly states the tool analyzes text with NLP metrics (language detection, sentiment, topic extraction, entity recognition, readability) for content auditing and classification. It explicitly distinguishes itself from page-reading tools by saying 'Takes text, not a URL', separating it from scrape and related siblings.

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

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

It gives explicit when-to-use guidance ('NLP metrics on text you already hold') and when-not-to-use guidance ('Not for reading a page'), even naming the exact alternative path: 'scrape returns the markdown to pass in'. This provides actionable routing to 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds value beyond that by disclosing cost (5 credits), the sync/async behavior difference, the webhook requirement for async, and the existence of get_batch_results. It doesn't contradict annotations. A small gap is that it doesn't describe return format or pagination, but that is secondary given the annotation coverage.

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 plus an example. It front-loads the purpose, immediately states the key usage constraint (no looping), and provides a concrete example. Every sentence earns its place; 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?

For a complex tool with 15 parameters, nested objects, and no output schema, the description covers the most critical decision points: when to use it, how to scale via modes, cost, and an example. It doesn't explain all parameters, but the schema does that. It also points to get_batch_results for async retrieval. Slight gap: it doesn't describe the sync return structure or pagination, but that is not explicitly required given the schema and the tool's nature.

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 every parameter is described in the schema. The description adds an example that illustrates urls, formats, and maxConcurrency, and it explains the mode parameter's semantics (sync vs async) beyond the enum. However, it doesn't clarify other parameters like redact_pii, extractionSchema, or jobOptions, relying on the schema. This is adequate given the schema's thoroughness.

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 2-50 URLs in one call' and names target content types. It immediately distinguishes itself from siblings by stating it is 'Not for one URL (scrape) or for discovering URLs (map_site)', so an agent can separate it from the many sibling scraping tools without opening schemas.

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

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

It gives explicit when-to-use guidance: 'Never loop scrape over a URL list' and explains the mode choice (sync for up to ~25 URLs, async with webhook for larger batches). It names the alternatives (scrape, map_site) and the condition that selects them, plus an example call. This is comprehensive routing guidance.

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

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

Annotations cover only the safety profile (readOnlyHint=false, openWorldHint=true, idempotentHint=false). The description adds the operational traits an agent actually needs: session expiry (600s absolute / 300s idle), ref invalidation on navigation, robots.txt enforcement, screenshot resource URIs, and per-operation credit costs. This is well beyond what annotations provide.

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

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

Front-loaded with purpose, the operating loop, then rationale, alternatives, operations, constraints, and cost. Dense and mostly every sentence earns its place, though the operations enumeration partially duplicates schema descriptions and the end-of-paragraph cost/expiry detail could be tightened.

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

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

With 19 parameters, nested objects, no output schema, and seven sub-operations behind one entrypoint, this description supplies the missing glue: the workflow order, ref lifecycle, expiry semantics, credit costs, and a concrete call example. An agent can sequence a correct multi-call session from the description alone.

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

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

Schema coverage is 100% and each parameter already carries an operation prefix, so the baseline is 3. The description goes further by mapping operations to their parameter sets ('open (url, stealth, ttl, activity_ttl, viewport)') and by explaining ref semantics ('stable refs (@e1, @e2 ...)') and ref lifetime, which the schema cannot express. It does not document timeout, continue_on_error, or max_inline_chars behavior beyond what the schema says.

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 ('drive a browser across several calls') and immediately establishes what makes it distinct: persistent state (page, cookies, login) across calls. It names the loop of operations and explicitly contrasts itself with scrape and scrape_with_actions, so an agent can identify it without opening the schema.

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

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

Gives explicit when-to-use ('look before each step instead of committing to a whole chain up front, so a wrong selector costs one call rather than all of them') and explicit when-not-to-use ('Not for a page that renders without interaction (scrape), and not for an interaction you can write out in advance - that is one scrape_with_actions call for 5'). Names both alternatives and the selecting condition.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, covering safety and variability. The description adds cost and the example, but doesn't disclose additional behavioral traits like pagination, handling of redirects, or result format (though the output schema exists). It adds some context beyond annotations, but not rich detail.

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 plus an example, with zero fluff. The purpose is front-loaded, followed by exclusions, cost, and a clear example. Every sentence earns its place.

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

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

Given the high complexity (17 parameters, nested objects, output schema present), the description covers the essential decision-making (when to use, when not, cost, example) and relies on the schema for parameter details. It could mention that it's a heavy operation or that results may be large, but the output schema and schema descriptions fill most gaps. It is complete for an agent to decide whether to invoke it.

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

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

Schema description coverage is 100%, so the schema documents every parameter in detail. The description itself includes a concrete example using url, max_depth, max_pages, and extract_content, which reinforces usage, but doesn't add semantic meaning beyond what the schema already provides. 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 uses a specific verb ('fetch many pages by following links') and clearly distinguishes itself from sibling tools (scrape, batch_scrape, map_site) by naming what it is not for. It also gives concrete use cases (knowledge base, docs index, full-site audit), so an agent can immediately understand its scope.

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

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

Explicitly states when NOT to use it (single page, known URL list, URL discovery) and names cheaper alternatives (map_site). It also provides cost context ('4 credits base, grows with page count'), which helps the agent decide if it's the right 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?

Beyond the readOnly/openWorld annotations, it discloses that results are stored at crawlforge://research/{sessionId}, that maxUrls > 50 triggers a confirmation/elicitation step, that cost is 10 credits base and grows with maxUrls, and that synthesis depends on LLM keys or Ollama being configured. These details go well beyond what the annotations provide, and there is no contradiction.

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 composed of only a few high-value sentences: purpose, routing, exclusions, behavioral notes, cost, and an example. Every sentence earns its place, and the core purpose is front-loaded before alternatives and cost details.

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 20-parameter tool with no output schema, it covers essential context: choice criteria, execution pipeline, confirmation behavior, cost, storage location, and LLM configuration dependency. It does not explicitly describe the report's return shape, but the schema's max_inline_chars documentation about preview plus result_handle partially fills that gap.

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

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

Schema coverage is 100%, so the baseline is 3; the description adds meaningful semantics around maxUrls (confirmation threshold and cost scaling) and includes a concrete example mapping topic, maxUrls, and researchApproach. Other parameters are left to the schema, but the schema already documents them thoroughly.

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 ('exhaustive multi-source research on a topic') and details the pipeline: web search, source fetch/analysis, conflict detection, and report synthesis when LLM/Ollama is configured. It also explicitly differentiates this tool from search_web and scrape, so an agent can distinguish it from relevant siblings.

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

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

It states when to use it ('any report or comparison built from several sources'), names alternatives explicitly (search_web and scrape), and gives clear negative guidance ('Not for a question one search answers' or 'a single page'). It also declares it preferred over built-in deep-research skills, leaving no ambiguity.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds valuable behavioral context beyond that: the 2-credit cost, the fact that it returns clean markdown, and the overlap with scrape's default behavior. It doesn't cover response format or pagination, but the safety profile is handled by 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?

Every sentence earns its place: purpose, exclusions with alternatives, cost, and example. The most critical information (what it is and when not to use it) is front-loaded. No filler or repetition.

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

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

For a tool with 6 parameters and nested objects, the description covers the core decision (when to use), cost, and a minimal invocation example. Advanced parameters like options, redact_pii, and max_inline_chars are left to the schema, which documents them thoroughly. The description is complete enough for correct basic invocation and tool selection.

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

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

Schema description coverage is 100%, so baseline is 3. The description includes a concrete example using the required url parameter, which reinforces its meaning, but it doesn't add semantic detail for the other parameters beyond what the schema already provides. The example is useful but not additive to the schema's own 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?

States a specific verb (extract) and resource (readable body of an article-style page with boilerplate removed). Distinguishes itself from siblings by explicitly naming scrape and fetch_url as alternatives for different scenarios, so an agent can tell them apart.

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

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

Provides explicit when-to-use (RAG, summarization, LLM context) and when-not-to-use (JS-rendered pages, after fetch_url) with named alternatives. Also notes that scrape with onlyMainContent:true already returns the same clean markdown, eliminating redundant calls.

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

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

The description adds meaningful behavioral context beyond annotations: 'One fetch, exact values, no LLM in the extraction path, so nothing can be fabricated,' and warns payloads are often over a megabyte. It also discloses the credit cost, which is useful operational behavior. No contradiction with the readOnly/idempotent/destructive 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?

Every sentence earns its place: purpose, framework guidance, behavioral guarantee, payload-size warning, non-usage exclusion, cost, and a realistic example. It is dense but well organized, front-loading the core decision before the details.

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

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

Despite having no output schema, the description gives enough to invoke the tool correctly: when to use it, how to scope large results with path, an executable example, and clear non-targets. The schema covers parameter mechanics like robots and inline limits, so no critical guidance 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?

Input schema coverage is 100%, so the baseline is 3. The description adds value by explaining why path matters ('Payloads are routinely over a megabyte') and giving a concrete path example ('next_data.props.pageProps'), going beyond the schema's syntax note.

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: extracting data from a page's embedded JavaScript state rather than rendered HTML. It explicitly identifies frameworks (Next.js, Nuxt, Apollo, Redux, application/json blocks) and distinguishes itself from scrape with 'Not for the rendered text of a page (scrape) or for sites built without a framework payload.'

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 opening line states the exact condition for use: 'Use this when a page's data lives in its embedded JavaScript state rather than its rendered HTML.' It also gives an explicit exclusion and the name of the alternative (scrape), plus an example showing the intended call shape.

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

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds value beyond annotations by disclosing the exact filter_external behavior, a cost of 1 credit, and the implicit single-page scope. It does not contradict any annotation.

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?

Every sentence earns its place: purpose, use cases, filter behavior, exclusions, cost, and an example. It is front-loaded with the primary purpose and routes around alternatives before ending with a concrete invocation. No filler or repetition.

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

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

For a single-page link listing tool with no output schema, the description is complete: it names use cases, flags exclusions, gives the cost, and provides a copy-paste example. An agent has everything needed to call it correctly and to decide when it is the wrong choice.

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 all 5 parameters. The description reinforces the filter_external behavior and provides a concrete example, but adds no meaning beyond what the parameter descriptions already supply. Baseline 3 is appropriate.

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

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

States a specific verb and resource ('list the hyperlinks on one page') and immediately enumerates concrete use cases (crawl seed list, broken-link audit, related resources). The description also distinguishes this tool from map_site and scrape, so an agent can select it confidently among many similar siblings.

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

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

Explicitly says when NOT to use it: not for a whole site (map_site), and not alongside a scrape of the same URL because scrape formats:['markdown','links'] returns both in one fetch. It also gives an example call, which is direct usage guidance. This is the clearest possible routing information.

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

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

The annotations already declare readOnly, idempotent, and non-destructive behavior, so the safety profile is covered. The description adds the cost detail ('1 credit') and a note about avoiding duplicate scrapes, but it does not disclose behavioral aspects like response shape, robots.txt handling, or user_agent defaults beyond what annotations already imply.

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 core purpose appears first, followed by the anti-duplication warning, cost, and a usage example. Every sentence earns its place and there is no redundant wording.

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

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

Given the tool's moderate complexity and the fully documented input schema, the description covers the essential operational details: purpose, when to avoid it, cost, and an example call. It does omit an explicit description of the response envelope, but the listed metadata fields already hint at the return content, so nothing critical is missing.

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

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

Schema description coverage is 100%, so the schema already documents all four parameters. The description only shows a URL example and does not go beyond the schema's parameter documentation, so it meets the baseline but provides no additional parameter-level 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 names a specific verb and resource: extracting a page's SEO metadata, then enumerates exactly which fields are included (title, meta description, Open Graph tags, canonical URL, schema.org data). It also distinguishes itself from the scrape tool by explicitly warning against using it alongside a scrape of the same 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?

It states precisely when to use the tool ('for a page's SEO metadata only') and when not to use it ('Not alongside a scrape of the same URL'). It names the alternative and the exact condition that makes it better: scrape with formats ['markdown','metadata'] returns both in one fetch.

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

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

The annotations already cover readOnly, idempotent, and non-destructive traits. The description adds genuinely useful behavioral disclosure beyond those: 'Uses an LLM by default; falls back to CSS selectors when no LLM is configured' and 'Cost: 3 credits.' There is no contradiction with the annotations.

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

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

The description is front-loaded with a clear purpose and uses only three sentences plus an example. Every sentence earns its place: the when-to-use rule, a priority alternative, the cost, and a complete invocation example. Nothing is redundant 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?

With an output schema present, the description does not need to explain return values. It fully covers how to select this tool versus alternatives, how to invoke it (example), the cost, the default LLM behavior, and its fallback path. Combined with the 100% schema coverage, this is a complete definition.

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 example in the description demonstrates the `url` and `schema` parameters in context. However, the description mostly restates the schema's own parameter descriptions and does not add deeper explanation for parameters like `llmConfig`, `selectorHints`, or `verify_numbers` beyond what the input schema already provides.

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's first sentence states exactly what the tool does: 'Use this to get a specific data shape from a page using a JSON schema.' It gives the precise condition 'when you can describe the fields but not their selectors' and cites concrete use cases (product details, job listings, event data), which clearly distinguishes it from the sibling scrape_structured.

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 sets when to use it ('when you can describe the fields but not their selectors') and when not to use it ('Not for pages with stable markup whose selectors you know') and directly names the alternative: scrape_structured. It also notes the default LLM use and the fallback path, so an agent is not left to guess.

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

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

Annotations already declare readOnly, idempotent, and non-destructive behavior, so the bar is lower. The description adds useful behavioral context: it strips tags/scripts/styles, targets static HTML rather than JS-rendered pages, costs 1 credit, and this is the cheapest read. No contradiction with annotations.

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

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

Three sentences with no waste: usage, exclusions, cost, and an example are all packed efficiently. The most important scoping information is front-loaded, and 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 read-only extraction tool with 7 parameters fully described in the schema and no output schema, the description covers everything needed to select and invoke it correctly: when to use it, alternatives, cost, format choice, and a concrete example. 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?

Schema description coverage is 100%, so the baseline is 3; the schema already documents each parameter thoroughly. The description adds marginal value by pointing out the markdown format for RAG and showing an example call, but it does not provide substantial meaning beyond the schema.

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

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

States exactly what the tool returns: a page's plain text or markdown with tags, scripts, and styles removed, and identifies it as the cheapest read of a static HTML page. It also names sibling tools (extract_content, scrape) and the conditions that select them, so the agent can distinguish it from alternatives.

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

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

Explicitly says when to use it (static HTML plain text/markdown, cheap read, RAG with markdown) and when not to (article pages, JS-rendered pages, or when links/metadata are needed), naming the alternative for each exclusion. The example call also gives concrete usage guidance.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, covering the safety profile. The description adds valuable behavioral context beyond annotations: default Ollama local endpoint with no API key, automatic model selection, cloud providers needing matching API keys, and cost (3 credits plus the provider's own charge). No contradiction with annotations; the extra context justifies a score above baseline.

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

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

The description is information-dense but every sentence earns its place: purpose, default behavior, model/provider overrides, exclusions, when to call a sibling, and cost. The core purpose is front-loaded, and there is no fluff or restatement of the tool name.

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 10 parameters and no output schema, the description covers the key invocation decisions: required prompt, url/content mutual requirement, provider/model defaults, and when to use alternatives. It omits the return-value shape, but the absence of an output schema makes this a minor gap; the essential behavioral and routing information is present.

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

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

Schema coverage is 100%, so the schema documents every parameter. The description adds meaning beyond the schema by specifying default model names per provider, explaining that 'auto' resolves to Ollama, and clarifying that url/content are mutually required. This is genuinely additive to the structured field descriptions.

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

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

The description states a specific verb and resource: 'Extract data from a URL or text using a natural-language prompt.' It explicitly names the siblings it is not for ('Not for known selectors (scrape_structured) or a schema-shaped result (extract_structured)'), so an agent can distinguish it from extract_structured and scrape_structured without opening their schemas.

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

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

The description gives explicit when-to-use context: use for natural-language extraction from URL/text, not for selector-based scraping or schema-shaped results, and call list_ollama_models only when a model name is rejected. Provider selection (ollama vs openai/anthropic) and API key requirements are also made clear, leaving little to inference.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. The description adds significant behavioral context beyond that: it discloses the cost (1 credit), the timeout and custom headers support, the unprocessed return of the body, and the specific behavior of respect_robots (recording false setting against API key and returning a warning). It also explains max_inline_chars behavior (preview + result_handle). No contradictions.

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

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

The description is three sentences, front-loaded with the core purpose and immediately contrasting with siblings. Every sentence earns its place: purpose, exclusions, features, cost, and an example. No fluff, no repetition of schema details.

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 6 parameters and no output schema, the description covers all necessary decision points: what to use it for, what to avoid, what it returns, cost, and edge cases (large results). It also names the sibling tools an agent might consider. The absence of an output schema is mitigated because the description explicitly states it returns the body and also mentions status, headers, and response time. The example shows a minimal valid call. Everything an agent needs to invoke correctly is present.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds value by hinting at typical header usage ('e.g. auth tokens') and by including an example that demonstrates timeout usage. It also clarifies the implication of respect_robots and max_inline_chars in context, going slightly beyond the schema descriptions. However, the schema already fully documents each parameter, so the increment is modest.

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

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

The description states a clear verb-resource pair ('fetch a URL') and immediately distinguishes itself from siblings by specifying exactly what it returns (raw body, status, headers, response time) and what it does not (HTML rendering). It names the sibling 'scrape' and the extract_* family as alternatives, making differentiation explicit.

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

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

The description gives explicit when-to-use guidance (raw HTTP body, API responses) and when-not-to-use (HTML for reading, JS-rendered or bot-protected pages), and points to alternatives: 'scrape returns markdown from one fetch' and 'scrape, then stealth_mode'. It also includes a concrete example call, reinforcing correct invocation.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context: the cost (5 credits) and a concrete example call. It doesn't describe side effects or rate limits, but the annotations cover the key behavioral traits. The cost disclosure is a valuable addition beyond annotations.

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

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

Three sentences with zero waste. The core purpose is front-loaded, the exclusion is stated early, and the cost plus example are packed efficiently. 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 tool with 5 parameters, 100% schema coverage, and no output schema, the description covers the essential context: what it does, when to use it, cost, and an example. The only minor gap is that it doesn't describe what the return value looks like, but with no output schema and rich parameter schema, the description is largely complete for an agent to select and 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 description coverage is 100%, so the schema already documents all parameters. The description adds a concrete example with the url parameter, which is helpful, but doesn't add meaning beyond the schema for the other parameters. Baseline 3 is appropriate when the schema does the heavy lifting.

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

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

The description states a specific verb ('generate'), a specific resource ('llms.txt file for a website'), and the purpose ('standard that tells AI models how to interact with a site's content'). It also explicitly distinguishes itself from reading an existing llms.txt by pointing to fetch_url on /llms.txt. This is a clear, specific purpose that differentiates it from siblings.

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

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

The description explicitly says when to use this tool ('for site owners preparing for AI discoverability') and when not to use it ('Not for reading a site's existing llms.txt (fetch_url on /llms.txt)'). It names the alternative tool and the condition that selects it. This is explicit usage guidance.

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

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

Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds valuable behavioral context beyond annotations: the batch must be already-paid, polling is only for async jobs, and there is a per-call credit cost. It does not describe the return envelope, but the schema's max_inline_chars description partially covers the preview/handle 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 compact and front-loaded: it states the core purpose, key constraints, cost, and a concrete example in just three sentences. There is no filler or repetition of schema content.

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

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

For a simple paginated read tool with full schema coverage and supportive annotations, the description covers the essential operational details: how to identify the batch, when to poll, cost, and a working example. The large-result fallback is documented in the schema, so nothing critical is missing 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 coverage is 100%, so the baseline is 3. The description adds value with a concrete invocation example showing batchId, page, and pageSize in context, reinforcing the relationship between the batchId and the batch_scrape call. It does not add detail for max_inline_chars, but the schema already documents it fully.

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

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

The description clearly states a specific verb ('Retrieve'), a specific resource ('paginated results for a batch_scrape job'), and the key identifier ('batchId'). It also explicitly distinguishes itself from a scraping tool, which sets it apart from siblings like batch_scrape and scrape.

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 explicit when-to-use guidance: poll only async jobs, and don't use for sync batches whose results were already returned. It also clarifies it re-reads an already-paid batch and notes the cost, leaving no ambiguity about when this tool applies.

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

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

Annotations already declare readOnly, idempotent, and non-destructive, so the bar for additional behavior is lower. The description adds meaningful context: the Ollama prerequisite (localhost:11434 or $OLLAMA_BASE_URL), the associated credit cost, and the 'installed locally' scope. It doesn't cover error behavior, but that is a minor gap for a simple read-only list.

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

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

Three sentences, each earning its place: the core operation, the usage guidance, and the prerequisite/cost. The key information is front-loaded and there is no wasted text.

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

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

For a zero-parameter, read-only list tool, the description covers purpose, when to use, the alternative, the infrastructure prerequisite, and cost. The output is implied by the purpose ('list... to choose a model'), so nothing needed for correct invocation is missing.

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

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

There are zero parameters, so the baseline is 4. The description correctly avoids inventing parameter guidance and instead clarifies the output's intended use ('model' value for extract_with_llm), which adds value beyond the empty schema.

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

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

The description states a specific verb ('List'), a precise resource ('Ollama models installed locally'), and a clear purpose (choosing a model for extract_with_llm). It is distinct from all sibling tools and leaves no ambiguity about what the tool returns.

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

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

It explicitly says the tool is not needed before every extraction, names the alternative behavior (extract_with_llm picks a default), and gives two concrete conditions for calling it: a rejected model name or a need for a specific size. This is excellent when-to-use guidance.

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

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

Annotations already mark the tool as non-read-only, non-idempotent, and non-destructive. The description adds a stateful side effect (configure_country sets country context for subsequent scraping calls), clarifies that it is not a normal page fetch, and discloses the 2-credit cost. This adds useful context beyond the annotations, though it stops short of describing effects of the other operations.

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 compact sentences plus one illustrative example. Purpose is front-loaded, cost and exclusion are included without bloat. The opening clause is slightly run-on, but the overall length and structure are appropriate.

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 fully supports the configure_country path and excludes ordinary scraping, but the tool exposes eight operations and has no output schema. The other operations such as localize_search, auto_detect, handle_geo_blocking, and get_stats are not mapped to use cases or expected outputs, which is a clear gap for a multi-operation tool.

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

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

Schema coverage is 100%, so the baseline is 3. The description adds value with a concrete invocation binding operation, countryCode, and language, and explains what configure_country does with those parameters. The rest of the parameters are already well documented in the schema.

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

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

States concrete use cases: scraping geo-restricted content, emulating a locale/timezone, region-specific pricing, geo-blocks, and localized search. It also distinguishes itself from the 'scrape' sibling with 'Not for an ordinary page read (scrape)' and provides a concrete example so an agent can tell what the tool is for.

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

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

Gives explicit when-to-use conditions (geo-restricted content, locale/timezone emulation) and an explicit when-not ('Not for an ordinary page read (scrape)'). The example shows exactly how to invoke the primary configuration operation. It does not map out all eight operations, but the tool-vs-alternative decision 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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds behavioral detail beyond annotations: it 'reads sitemap.xml when available, otherwise follows links' and notes the 2-credit cost, which helps set expectations about how the tool operates and what it costs.

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

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

The description is front-loaded with the core purpose, followed by sibling exclusions, cost, and an example. It is slightly longer than strictly necessary because the example overlaps somewhat with the schema, but every sentence earns its place and none merely restate the name or schema.

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

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

For a 10-parameter tool with an output schema and rich annotations, the description covers the essential context: what the tool returns conceptually, when to use alternatives, the cost, and an example. It does not explain the domain_filter or search parameters, but those are fully documented in the schema, so the description is complete enough for routing and 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 schema already documents all 10 parameters. The description adds minimal parameter semantics beyond the schema, mostly via the example invocation that references include_sitemap and max_urls. This matches the baseline of 3 when the schema carries the parameter documentation burden.

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

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

The description opens with a specific verb and resource: 'list a site's URLs without fetching page bodies.' It explicitly distinguishes itself from siblings by naming what it is not for ('not for page content (scrape, or crawl_deep for many pages) and not for the links on one page (extract_links)'). An agent can immediately tell this apart from scrape, crawl_deep, and extract_links.

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 explicit when-to-use guidance ('list a site's URLs without fetching page bodies') and explicit exclusions with named alternatives. It also includes cost and a concrete example, which helps an agent decide if invoking this tool is appropriate.

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

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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description does not contradict them. Beyond that, it discloses cost (2 credits), the refusal of unsupported body types ('refused by name'), the fact that a PDF under sourceType 'url' still reaches its parser, and the return contents (structured sections, metadata, word count). This is substantial additional behavioral context that aids the agent in understanding side effects and edge cases.

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 primary use case in the first sentence, then adds supporting details (edge cases, alternative, cost, example). It is slightly verbose but every sentence carries unique information. The structure is logical and it remains readable without being padded.

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 moderate complexity (7 params, nested object, enum, cost, redaction behavior), the description covers the key decision points: what it does, what it doesn't accept, an example, and the output shape. The output schema is not provided, but the description mentions return fields. It lacks explicit details on all option combinations, but the schema descriptions fill that gap. It is complete enough 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 coverage is 100% for all 7 parameters, so the structured descriptions are comprehensive. The description adds value by providing an illustrative example (source + sourceType), clarifying the nuance that sourceType 'url' can still process PDFs, and implying the meaning of source and sourceType through the example. While it doesn't detail options like maxPages or redact_pii (handled in schema), this is above the baseline for high-coverage schemas.

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 ('extract text') and resource ('PDF or DOCX URL or file'), names the target document types, and gives an example call. It explicitly distinguishes itself from web scraping ('Not for ordinary web pages (scrape)') and clarifies an edge case (HTML URL accepted but handled differently). This is precise and actionable.

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 for when to use this tool ('research papers, contracts, reports') and explicitly excludes ordinary web pages ('Not for ordinary web pages (scrape)'). It also gives a concrete usage example. It does not mention alternative tools by name (e.g., extract_text) but the exclusion and example are sufficient for an agent to select it correctly in most cases.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable behavioral context: the 1-hour retention window, the preview behavior, the credit cost, and the semantics of each operation (search, slice, lines, json_path). This goes beyond the annotations without contradicting them.

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

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

The description is a single dense paragraph that front-loads the primary use case and ends with an example. It is somewhat lengthy but every sentence earns its place by covering scope, operations, exclusions, cost, and an example. A more structured layout could improve skimmability, but it remains 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?

Given an output schema exists (not shown but present), the description need not detail return formats. It covers the main trigger, all operations, exclusions, cost, and handle validity. It does not explicitly mention error cases (e.g., expired handle), but the output schema likely handles those. Overall, it's sufficiently complete 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 coverage is 100%, so every parameter is documented. The description adds operation-specific meaning (e.g., 'search finds a literal query with offsets and context', 'json_path reads one subtree') and a usage example that clarifies how parameters combine. This enriches understanding beyond the schema's basic field descriptions.

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

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

The description clearly states the tool reads truncated results, identifies the trigger condition (truncated: true with a result_handle), and explicitly distinguishes itself from fetching tools by instructing not to call the original tool again. It also names sibling tools indirectly and lists distinct operations, making its scope unambiguous.

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

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

The description provides explicit when-to-use (truncated results) and when-not-to-use (whole results, not a fetching tool) guidance. It also gives a concrete example call and notes the 1-hour handle validity, which helps the agent decide when to invoke this tool versus alternatives like fetch_url or crawl_deep.

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

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

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description discloses substantial behavioral traits: it reads from Arctic Shift and PullPush archives, explains that Reddit blocks direct scraping, details fallback logic with fallback_used, describes timeout retry windows (7d, 3d, 1d) and window_applied, mentions PullPush's refusal of automated clients since August 2026, and notes the cost of 5 credits. This adds meaningful context that annotations alone cannot 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 long but information-dense and front-loaded with the core purpose and mode overview. It covers many nuances without redundancy. It could be slightly trimmed, but every sentence earns its place given the tool's complexity, and the structure flows logically from general usage to specifics like fallbacks and exclusions.

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 is comprehensive for a tool with 10 parameters and 3 enums. It explains all modes, backend behaviors, fallback mechanisms, constraints (e.g., PullPush refusal, Reddit blocking), cost, and provides a full example. With an output schema present, no additional return-value explanation is needed, and nothing critical is missing for an agent to invoke it correctly.

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

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

Schema coverage is 100%, so the schema already documents all parameters clearly. The description adds value by providing a concrete example (reddit_search({query: "best mechanical keyboard", subreddit: "MechanicalKeyboards", limit: 10})) and clarifying mode-specific behavior (e.g., posts matches title+selftext, comments matches body, thread requires link_id). While not essential, this contextualization helps an agent understand how parameters interact in practice.

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

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

The description opens with a clear statement of purpose: 'Use this to search Reddit posts or comments, or read a full comment thread.' It names the resource (Reddit), the verbs (search, read), and distinguishes itself from siblings by explicitly stating it is not for reddit.com URLs via scrape/fetch_url, which separates it from the generic fetch_url and scraper 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?

The description explicitly states when to use the tool and provides explicit exclusions: 'Not for reddit.com URLs via scrape or fetch_url (blocked) - use mode:"thread" with the post's link_id.' It also explains when to use each mode, the fallback behavior, and the two-step discovery for unscoped searches, giving clear guidance on usage contexts and alternatives.

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

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

Annotations declare readOnlyHint=true, idempotentHint=true, destructiveHint=false; the description adds rich behavior beyond them: per-format warnings never fail the whole call, onlyMainContent strips via Readability, escalate runs the plain fetch first and only retries in the stealth browser when walled, plus cost and example. No contradiction with annotations.

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

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

Long but dense: every sentence earns its place. Purpose and core behavior are front-loaded, then formats, exclusions, cost, and an example. The grouped clause style keeps a high-information paragraph readable without waste.

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 12-parameter tool with nested objects and multiple formats, the description covers selection criteria, exclusions, cost model, partial-success behavior, escalation, and an example. An output schema exists, so return values need no explanation; the schema fills any remaining parameter-level 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?

Schema description coverage is 100%, so the baseline is 3. The description adds real meaning beyond the schema: explains branding as static design tokens, screenshot returns crawlforge:// resources, highlights query costs 1 extra credit with no model, and gives a concrete call example. It does not cover every parameter, but the schema already documents them well.

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?

Opens with a specific verb and resource: 'Use this to read one page'. Enumerates the exact formats, and explicitly distinguishes from siblings: 'Not for raw API/JSON bodies (fetch_url), a page that needs a click or login (scrape_with_actions), or 2+ URLs (batch_scrape)'.

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?

States when to use this tool and names concrete alternatives with the conditions that select them: fetch_url for API/JSON, scrape_with_actions for click/login, batch_scrape for 2+ URLs. Also says 'Preferred over the client's built-in web fetch' and tells when to set escalate:true.

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

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

Beyond the readOnly/idempotent annotations, it discloses the critical row-alignment gotcha (data.price[0] need not belong to the same row as data.name[0]), the cost of 2 credits, and the meaning of row_selector. This is exactly the kind of non-obvious behavior an agent needs before invoking.

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

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

The description leads with the decision rule, then covers exclusions, the key behavioral warning, cost, and an example with no filler. Every sentence contributes necessary operational 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?

Despite having no output schema, the description explains the shape of returned data (parallel arrays vs row-aligned objects) and gives enough context for an agent to invoke correctly. Together with the rich input schema, this is complete for a read-only extraction tool.

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

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

The schema already covers all 6 parameters in detail, so the baseline is 3. The description adds value with a concrete usage example and clarifies how selectors and row_selector work together, but it does not substantially redefine the parameter meanings beyond the schema descriptions.

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

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

The description names a specific verb and resource (scrape structured data with exact CSS selectors) and differentiates it from LLM-driven extraction. It is immediately clear this tool is for well-structured pages with known markup.

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 an explicit when-to-use condition (you know exact selectors, consistent markup) and states what it is not for (varying markup or unnamed selectors), pointing to extract_structured / LLM-driven extraction as the alternative. This lets an agent select correctly among many 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?

Beyond annotations (readOnlyHint, openWorldHint, idempotentHint), the description discloses cost (1 credit), robots.txt handling including the recording of respect_robots:false against the API key, the retired-template behavior with the reason returned, auto-mode naming its template choice, and record-count differences between page and list templates. No contradiction with annotations exists.

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

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

The description is long but front-loaded with the purpose and organized into modes, template catalogs, retired templates, list connectors, exclusions, cost, and example. Every sentence earns its place in terms of content, but the density is high enough that a few aggressive editors might request trimming; it remains well above the minimum viable level.

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

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

With no output schema present, the description compensates by stating return shapes: 'one record' for page templates, 'N records' for list connectors, 'names its choice' in auto mode, and 'returns the reason' for retired templates. It also covers cost, robots behavior, a full parameter-driven example, and the full catalog of supported sites, making it complete for an agent to call 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?

Although schema coverage is 100%, the description adds substantial meaning: it defines the three template modes, clarifies when url is required, gives concrete template IDs for both page and list connectors, and provides a full example invocation with params. It also explains philosophy of user_agent honesty and respect_robots decisions, which the schema alone does not convey.

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 'Use this when you want structured data from a well-known site or platform API without writing custom selectors,' naming the verb, resource, and value proposition. It differentiates itself from siblings by explicitly naming scrape as the alternative for sites without a template, and by enumerating exactly which templates exist. No ambiguity remains about what the tool produces.

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

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

It starts with 'Use this when...' and later states 'Not for a site without a template (scrape),' giving an explicit exclusion and naming the alternative tool. It further explains when to use template:'list' to discover what exists and how params-driven list connectors differ from url-driven page templates, leaving no strategic decision 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?

Adds substantially beyond the annotations (which only cover readOnly/openWorld safety): cost of 5 credits, robots.txt enforced on every navigation, screenshots stored as crawlforge://screenshot/{actionId} resources, and the ref-invalidation rule after navigation. These are behavioral facts an agent cannot get from the schema or 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?

Front-loaded with the usage condition, then action list, chaining semantics, caveats, cost and a worked example. Dense rather than padded, though it is a long paragraph and the action enumeration partially duplicates the schema's type enum, costing some efficiency.

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 14-parameter, nested-object tool with no output schema, the description covers the essential mental model (chain of actions, ref lifecycle, stealth, robots, credits, screenshot resources) and a working example. It is slightly thin on what the overall response contains and on formAutoFill/extractionOptions behaviors, but nothing critical for correct invocation is missing.

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

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

Schema coverage is 100%, so the baseline is 3, but the description adds real semantics: it explains the snapshot-then-target-refs pattern (@e1, @e2), that later actions target refs 'instead of guessing CSS selectors', and that navigation invalidates refs. It also surfaces browserOptions.stealth and the example chain, going beyond field-level docs.

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

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

States a specific verb+resource (interact with a page, then scrape) and immediately names the trigger conditions - SPAs, login-gated content, multi-step flows - with a concrete action list. An agent can distinguish this from sibling 'scrape' and 'stealth_mode' without opening either schema.

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

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

Explicitly routes the agent: 'Not for pages that render without interaction (scrape) and not as the first attempt on a blocked site (stealth_mode operation:"scrape")'. Both the when and the when-not are stated, with the sibling alternative named for each case.

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

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

Annotations already cover read-only, idempotent, non-destructive behavior. The description adds meaningful behavior beyond that: cost of 5 credits per query, batched queries returning per-query results, and the useful behavioral hint that snippets often answer the question directly. No contradiction with annotations.

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

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

Every sentence earns its place: purpose, filter options, preference over built-in search, alternatives, batching behavior, cost, and an example. The most important purpose statement is front-loaded, and the description is dense without being padded.

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 rich schema (100% parameter coverage, output schema present) and strong annotations, the description fills the remaining gaps effectively: when to use which sibling, batching semantics, cost, and a usage example. An agent has everything needed to invoke 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 even without additional description. The description reinforces parameters like query, limit, time_range, and queries, and gives a concrete example, but it mostly restates what the schema already documents rather than adding new semantic depth.

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: 'find pages for a query' and explicitly lists the return contents (titles, URLs, snippets, optional metadata). It clearly distinguishes itself from siblings by naming search_web, scrape, reddit_search, serp_rank, and deep_research as different tools for different tasks.

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

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

Gives explicit when-to-use ('Preferred over the client's built-in web search'), when-not-to-use ('Not for a URL you already have', Reddit, Google rank, or multi-source reports), and names the exact alternative tool for each case. Also advises scraping only when the snippet is insufficient, which is practical routing guidance.

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

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

Annotations already establish readOnly/idempotent/non-destructive safety, so the bar is lower; the description adds valuable extras: external DataForSEO dependency, credential requirement, unconfigured failure mode, cost implications (5 credits, 0 unconfigured), and a no-retry rule. It does not disclose rate limits or pagination, but those are not necessary for a read-only lookup with an output schema.

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 tight and front-loaded: the core purpose appears first, followed by the sibling exclusion, operational caveat, cost, and example. Every sentence earns its place and none repeats the schema.

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

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

For a read-only external-SERP lookup with full schema coverage and an output schema, the description covers what an agent needs: purpose, scope, credentials, failure handling, cost, and example inputs. No critical behavioral or invocation context is missing.

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

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

Schema description coverage is 100%, so the schema already documents all 7 parameters. The description mainly reinforces keyword/target semantics and gives a concrete example with location_name, but does not add much meaning beyond that. Baseline 3 is appropriate.

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

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

States a specific verb and object: 'check where a domain ranks in Google's ORGANIC results for a keyword.' It explicitly distinguishes from Custom Search order and names the sibling search_web as the wrong tool for general search, so an agent can select it accurately.

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

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

Gives explicit routing guidance: 'Not for general search (search_web).' It also supplies operational prerequisites (DataForSEO credentials), defines the unconfigured failure value (configured:false), and tells the agent not to retry in that case. An example invocation further clarifies expected usage.

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

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

Beyond annotations that only flag readOnly=false, the description discloses concrete behavioral traits: randomized fingerprints, human behavior simulation, WebRTC/canvas spoofing, robots.txt respect on every navigation, one-shot teardown, and credit costs per operation. It also clarifies the side-effect of setting respect_robots to false (recorded against the API key). These add value beyond what annotations convey, though it doesn't cover rate limits or response error shapes.

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 dense paragraph, but every sentence carries decision-relevant information: when to use, what it does, operation modes, robots handling, cost model, and an example. Some structure (bullets for cost or operation modes) would improve scannability, but there is no filler or repetition.

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

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

Given 12 parameters, nested objects, multiple operations, and no output schema, the description covers the key contextual elements: the anti-bot problem it solves, the two usage patterns, robots.txt policy, credit cost, and an example. It does not explicitly state what the return payload looks like beyond 'returns the requested formats,' but the schema already describes max_inline_chars, redact_pii, and result_handle, so the missing piece is minor.

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

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

Schema coverage is 100%, so parameters are already documented, but the description enriches the operation parameter by explaining the one-shot scrape lifecycle versus the multi-step alternatives and gives a concrete invocation example ('stealth_mode({operation:"scrape", url:"https://example.com", formats:["markdown","links"]})'). It also attaches cost semantics to specific operations (5 credits per browser operation vs 1 for configure/enable/disable/get_stats/cleanup), meaning beyond the raw enum values in 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 opens with a specific trigger ('when a site blocks normal scraping – Cloudflare, Datadome, or other bot-detection systems') and names the resource and action ('Renders in a Playwright browser... operation:"scrape"'). It also explicitly distinguishes itself from the normal 'scrape' sibling with 'Not a first choice: try scrape first and switch here after a 403/429/CAPTCHA/challenge page or an empty shell.' This gives an agent a precise selection basis.

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

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

It provides explicit when-to-use guidance ('Use this when a site blocks normal scraping...'), a clear alternative ('try scrape first'), and the exact failure conditions that justify switching (403/429/CAPTCHA/challenge page or empty shell). It also separates the one-shot scrape path from the multi-step create_context -> create_page -> cleanup sequence, so an agent knows which operations fit single vs repeated work.

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

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

Annotations already declare the safety profile (readOnlyHint, idempotentHint, destructiveHint false). The description adds meaningful behavioral context: it accepts text not URLs, performs extractive or abstractive summarization, leverages Ollama/sampling for abstractive rewrites, and costs 4 credits. This goes beyond what annotations provide, though it doesn't disclose return format or edge-case 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 dense but organized: it starts with the core purpose, then input constraint, a usage heuristic, cost, and a concrete example. No sentence is wasted; the structure front-loads the main action and defers cost and example details.

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 tool with high schema coverage but an empty options schema, the description covers how to invoke it (text plus optional options), what input type to pass, and an example. It lacks an explicit statement of return value shape, which matters because there is no output schema; however, the core invocation requirements are adequately specified.

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% for both properties, but the 'options' property is an empty object in the schema, providing no usable semantics. The description compensates with an example showing summaryLength and summaryType, and clarifies that 'text' should be markdown from a scrape result. This adds real meaning beyond the schema, especially where the schema is empty.

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 ('condense') and names the resource ('text you already hold'), and distinguishes itself from URL-input tools by stating 'Takes text, not a URL.' It also lists output forms (briefing, comparison, shorter LLM context) and methods (extractive/abstractive), which clarifies exactly what it 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 provides explicit when-to-use context: summarizing text already held, and an explicit when-not-to-use: 'Not needed for text short enough to summarise in context yourself.' It also directs users to pass markdown from a scrape result, implying the preceding step. It doesn't name a specific sibling tool as an alternative, but it gives enough routing guidance.

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

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

Annotations only disclose readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false. The description supplements this with useful behavioral nuances: cost (3 credits), hosted CrawlForge servers with signed webhooks, and the expected pattern of repeated compare calls on the same URL. This goes beyond the annotation surface but does not detail side effects like state storage of baselines, which is part of the tool's open-world effect.

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?

It is only two sentences and still covers purpose, workflow, alternatives, cost, hosting option, and an example. Everything is relevant and tightly packed without redundancy; it reads like a well-specified instruction rather than an exhaustive manual.

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 very complex (15 parameters, nested objects, numerous sub-options). The description gives a good high-level flow but does not describe return values (no output schema present) nor elaborate on each of the many parameter groups (e.g., queryOptions, storageOptions, alertRuleOptions). Since there is no output schema, the description is expected to explain what the tool returns, which is missing here. It does provide enough to decide when to use it, but not enough for full understanding of its behavior across all operations.

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 carries all parameter descriptions. However the description adds a meaningful semantic: a clear workflow of operations (create_baseline, then compare) and a concrete example with url and operation. That enriches param selection beyond what the schema enum alone provides, so the description gives something extra.

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 the tool's specific verb (monitor) and resource (a URL), plus concrete purposes (competitor pricing, regulation updates, product availability) to distinguish from a one-off scrape. It explicitly says 'Not for a one-off read (scrape)', which differentiates it from sibling tools like scrape or fetch_url without needing to inspect 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?

It gives an explicit when-to-use pattern: 'Start with operation: create_baseline, then periodically use operation: compare' and adds a when-not ('Not for a one-off read (scrape)'). It also references the alternative `scrape` directly, qualifying as explicit alternative rejection and a clear usage flow.

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