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 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?

Annotations already establish readOnlyHint, idempotentHint, and destructiveHint. The description adds meaningful behavioral context beyond annotations by stating that it uses an LLM by default, falls back to CSS selectors when no LLM is configured, and costs 3 credits. It does not contradict the annotations. The description could also mention response/provenance behavior, but the parameter descriptions and output schema already cover some of that.

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 efficiently structured: it front-loads the core use case, gives examples, names the alternative, states the cost, and provides a minimal illustrative example. Every sentence earns its place, and nothing is redundant with 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?

Given the tool's complexity—9 parameters, nested objects, and an output schema—the description covers the essential decision context: when to use it, when not to, LLM fallback behavior, and cost. The parameter schema supplies the remaining details. A minor gap is that high-level behavioral knobs like verify_numbers and respect_robots are not mentioned in the description, but they are adequately explained in the schema.

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 parameter descriptions carry the baseline. The description adds a concrete example showing how to pass the schema object and required fields, which helps clarify the schema parameter. However, it adds no semantic detail for most other parameters like prompt, llmConfig, selectorHints, or user_agent, so it stays at the baseline.

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: 'get a specific data shape from a page using a JSON schema.' It clarifies the exact use case—describing fields but not their selectors—and gives concrete examples (product details, job listings, event data). It also explicitly distinguishes the tool from scrape_structured, removing any ambiguity.

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

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

The description provides explicit when-to-use guidance: use when 'you can describe the fields but not their selectors.' It names the alternative tool and the condition to choose it instead: 'Not for pages with stable markup whose selectors you know (scrape_structured, no LLM, cheaper).' This gives both positive and negative selection criteria.

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

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

Annotations already 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 carry readOnly/idempotent/non-destructive hints; the description adds the 5-credit cost and an example but does not disclose that the tool will fetch/analyze the target website or what 'generate' returns. No contradiction with annotations.

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

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

The description is compact and front-loaded: purpose, exclusion/alternative, cost, and example are delivered in three sentences with no filler.

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

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

For a tool with nested options and no output schema, the description still covers the core decision and invocation. It could more explicitly state that the tool crawls/analyzes the site and what the result payload looks like, but the cost, example, and annotation-provided safety profile make it sufficiently complete.

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

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

Schema description coverage is 100%, so parameter meaning is already documented. The description only gives a url example and adds no semantics for format, outputOptions, analysisOptions, or complianceLevel beyond the schema.

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

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

The description opens with a concrete verb and resource ('generate an llms.txt file for a website'), explains the standard and audience, and explicitly says what it is not for, differentiating it from fetch_url on /llms.txt.

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?

'Use this to generate...' and 'Not for reading... (fetch_url on /llms.txt)' give direct when/when-not guidance with the alternative named. The 5-credit cost and minimal invocation example also reinforce appropriate use.

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

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

Annotations already cover readOnly and idempotent hints. The description adds cost (1 credit), the need to poll only async jobs, and an example, which enriches the behavioral context without contradicting annotations. It does not discuss error handling, but that is not essential for this tool.

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

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

The description is concise with two sentences plus an example, front-loading the core purpose and then adding necessary clarifications. No redundant phrasing.

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

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

The tool is simple with three parameters, one required, and no output schema. The description covers purpose, usage constraints, cost, and gives an example, so an agent has enough to call it correctly without additional context.

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

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

Schema coverage is 100%, so the baseline is 3. The description includes an example with parameters but adds no new meaning beyond what the schema already documents for batchId, page, and pageSize. It does not clarify defaults or constraints beyond the schema.

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

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

The description clearly states the tool retrieves paginated results for a batch_scrape job using a batchId, and explicitly distinguishes it from a scraping tool. It uses a specific verb and resource, and the mention of 're-reads an already-paid batch' differentiates it from sibling scraping tools.

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

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

Provides explicit guidance on when to use: only for async batch jobs, and notes that sync batches already returned results. It also clarifies it is not a scraping tool, helping an agent choose between this and scraping siblings.

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

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

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, idempotentHint=true, and destructiveHint=false, which the description aligns with. It adds behavioral context beyond annotations by disclosing the credit cost (2 credits), the output shape (structured sections, metadata, word count), and the accepted input boundary (PDF/file, plus HTML URL). It does not mention large-result truncation or redaction side effects, but those are detailed in the schema, so the description adds reasonable value without 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?

Three sentences with no filler: purpose, scope boundary, return summary, cost, and a concrete example all earn their place. The essential information is front-loaded, and the example is compact and instructive.

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 7-parameter tool with no output schema, the description covers the primary use case, the input type, the core return values, and cost. The schema handles parameter-level details thoroughly. The only gap is that the description does not surface the max_inline_chars fallback behavior, which is meaningful for calling read_result, but this is adequately documented in the parameter schema.

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 in detail. The description adds little beyond the example call showing source and sourceType format; it does not clarify options, redact_pii, max_inline_chars, or user_agent semantics beyond what the schema 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?

States a specific verb and resource ('extract text from a PDF URL or file') with example use cases (research papers, contracts, reports), and differentiates from ordinary web scraping by naming 'scrape' as the alternative. The returned outputs (structured sections, metadata, word count) are also stated, making the tool's role 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?

Explicitly tells the agent when to use it ('extract text from a PDF URL or file') and when not ('Not for ordinary web pages (scrape)'). It names the sibling tool category 'scrape' and even clarifies the edge case that HTML URLs are accepted yet still treated as documents. The example call and credit cost further guide 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, 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?

Annotations already mark this as read-only/idempotent, but the description adds substantial behavioral context: the Arctic Shift archive backend, the web-discovery mechanism for unscoped keyword searches, the 7d/3d/1d retry windows, the window_applied report field, credit cost, and the blocking of direct reddit.com scraping. No contradiction with annotations.

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

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

The description is dense and front-loaded with the core purpose, and every sentence carries useful information. However, it is a single wall of text that could be more scannable with a short mode list or bullets; the length 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 10-parameter tool with multiple modes, the description covers all essential decision points: mode selection, scoping, comment-search discovery, timeout fallback, exclusions, cost, and an example. An output schema exists, so return structure is documented elsewhere.

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?

Even though schema coverage is 100%, the description adds meaning beyond the schema: it explains mode semantics, why link_id is required for thread mode, the source fallback behavior, and the meaning of window_applied. The example also illustrates valid parameter combinations.

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

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

The description opens with a specific verb and resource: 'search Reddit posts or comments, or read a full comment thread'. It names three distinct modes and explicitly contrasts itself with blocked scrape/fetch_url approaches, so an agent can distinguish it from the many sibling tools.

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

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

It gives direct when-to-use guidance: reddit.com URLs should not be scraped via fetch_url/scrape, and mode:'thread' with a link_id is the prescribed alternative. It also explains scoped vs unscoped behavior and the fallback retry windows, 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 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?

This is an annotation contradiction. The description says the tool performs browser interactions such as clicking, typing, form filling, and executing JavaScript, which can mutate remote page state or trigger side effects, while the annotations declare readOnlyHint=true and destructiveHint=false. The description does add useful context like robots.txt handling and screenshot storage, but the direct contradiction with the read-only hint is a serious behavioral disclosure failure.

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 use case, then presents exclusions, stealth guidance, robots behavior, cost, and an example in a logical order. It is longer than average, but the tool is complex with 14 parameters; the action list is slightly redundant with the schema enum, but it serves as a useful quick reference rather than fluff.

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

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

For a tool with 14 parameters, nested objects, and no output schema, the description covers usage triggers, exclusions, stealth mode, robots.txt, screenshot resources, cost, and a usage example. It does not explicitly describe the shape of the scrape result, the large-result result_handle path, or redaction behavior beyond what the schema describes, so a little more output-side context would make it fully complete.

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

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

Schema coverage is 100% and the schema itself provides detailed semantics for every parameter, including per-action enums, defaults, and constraints. The description adds a compact action list and a concrete example, but it does not explain parameter behavior beyond what the schema already provides, so the baseline 3 applies.

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

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

The description opens with a specific trigger: "Use this when you must interact with a page before scraping" and enumerates concrete use cases (login, click buttons, fill forms, scroll, wait). It names the resource and distinguishes itself from siblings by stating it is not for pages that render without interaction and not a first attempt on blocked sites.

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 is explicit about when to use the tool and when not to: it targets SPAs, login-gated content, and multi-step flows, and explicitly routes the no-interaction case to scrape and the blocked-site case to stealth_mode. This gives an agent clear decision rules without needing to inspect sibling tools.

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

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

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?

With readOnlyHint=false and idempotentHint=false, the description carries the burden of indicating stateful behavior; it does so by describing baseline creation, repeated comparisons, and a 3-credit cost. It also discloses that webhooks and scheduled monitoring are supported, adding context beyond the bare annotations. There is no contradiction with the annotation flags.

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 a small example, front-loading the core purpose before workflow and exclusions. Every sentence adds information; no filler.

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

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

The description adequately covers the basic monitoring workflow and cost, but the tool exposes many operations (get_history, get_stats, alert rules, dashboards, exports) that are not summarized. With no output schema, agents are given little about return values; however the schema descriptions fill most parameter-level gaps, so the description is minimally viable but not complete for the full feature set.

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%, giving a baseline of 3, but the description adds operational sequencing for the operation parameter (create_baseline then compare) and a concrete example invocation. This clarifies parameter use beyond the enum list, justifying a point above baseline.

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 ('monitor') and resource ('a URL for content changes over time'), lists concrete use cases, and explicitly distinguishes from sibling tools ('Not for a one-off read (scrape)'). This is a clear, non-tautological definition that an agent can separate from scrape and related one-off extraction 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?

Gives explicit workflow guidance: 'Start with operation: create_baseline, then periodically use operation: compare' and notes that repeated compare calls are expected. It also provides a when-not ('Not for a one-off read (scrape)') that names the alternative, though it doesn't enumerate all sibling distinctions.

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