momentic

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

No annotations provided, so description bears full burden. It discloses the tool fetches any public URL and returns a specific set of signals, including behavior for Cloudflare Markdown. However, it does not detail error handling, rate limits, or whether authentication is needed.

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

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

Description is two sentences, front-loaded with action and result, no unnecessary words. Efficient and easy to parse.

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

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

Given no output schema, description lists returned signals in detail, which is helpful. Lacks error handling or validation details, but adequate for a single-parameter 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?

Only one parameter (url) with schema coverage 100%. Description adds that the URL must be public and absolute, consistent with schema. No additional semantic value beyond what the schema provides.

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

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

Description clearly states the tool fetches any public URL and returns structured on-page SEO signals, listing specific signals. It differentiates from siblings like analyze_site and get_page by focusing on SEO diagnostics and mentions specific skills it powers.

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?

Description indicates the tool is designed for page-seo-score and geo-aeo-readiness skills, but does not explicitly state when to use it versus alternatives like analyze_site or search. No when-not guidance is provided.

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

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

Without annotations, the description must fully disclose behavior. It enumerates exactly which files and endpoints are checked and what is parsed (e.g., AI-bot rules from robots.txt). This provides high transparency, though it omits any potential side effects or return format details.

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 sentence that efficiently lists all probed endpoints. It is clear and front-loaded with the core action, but could benefit from slight restructuring for readability.

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 complexity of probing multiple signals and lacking an output schema, the description adequately lists what is checked but does not describe the output format or structure of results. This leaves some ambiguity for the agent.

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

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

With 100% schema description coverage, the schema already documents the domain parameter. The description adds context by clarifying it accepts 'example.com' or full URLs, but this is minor. The baseline score of 3 is appropriate.

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

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

The description clearly states the tool probes a domain root for specific AI-discovery signals, listing files and endpoints like /llms.txt, /robots.txt, /sitemap.xml, and well-known resources. This distinguishes it from siblings like analyze_page which likely targets individual pages.

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

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

The description implies usage for site-wide AI discovery but does not explicitly state when to use versus alternatives. No exclusions or comparisons to sibling tools are provided, leaving the agent to infer context.

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

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

With no annotations, the description fully discloses parallel probing, per-bot status, verdicts, and the blockingPattern field. It covers key behavioral aspects beyond the basic action.

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, well-structured paragraph with no wasted words. Every sentence adds value, and the information is front-loaded.

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

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

Given the absence of an output schema, the description adequately explains the return format (per-bot status, verdict, blockingPattern). It covers the essential outcomes for a tool of this complexity.

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

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

Schema coverage is 100%, and the description adds minimal extra meaning (default values). The baseline of 3 is appropriate as the schema carries most parameter documentation.

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

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

The description clearly states the tool probes a URL with multiple AI bot user-agents and returns per-bot reachability, distinguishing itself from robots.txt audits by revealing WAF/CDN-layer blocks.

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

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

The description explicitly contrasts with robots.txt audits, indicating when to use this tool. It does not provide a full list of exclusions or alternatives, but the context 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?

Description discloses behavior: pure computation, URL normalization defaults, and O(N^2) complexity for findMentions. However, no annotations are provided, and the description does not mention error handling, rate limits, or authentication requirements.

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

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

The description is informative but verbose (over 100 words). It is front-loaded with the main purpose but could be more structured (e.g., bullet points) to improve readability. Still, every sentence adds value.

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

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

Given the absence of an output schema, the description adequately lists the return types. However, it lacks information about error scenarios, input size limits, and authorization requirements. For a complex tool with nested objects, more completeness would be beneficial.

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 description adds significant value beyond the schema by specifying the exact input structure (e.g., 'url, internalLinks: [{href, anchor}], title?, bodyText?') and explaining the effects of options like hubCount, findMentions with performance note, and normalizeUrls. Schema description coverage is 50%, but the description compensates 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?

The description clearly states the tool performs pure graph computation over a crawled page set, listing specific outputs like orphan pages, hubs, authorities, PageRank-lite, etc. It distinguishes from sibling tools by its focus on multi-page link graph analysis.

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

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

No explicit guidance on when to use this tool versus alternatives like analyze_page or search. The description implies it's for link graph analysis but does not provide when-to-use or when-not-to-use instructions.

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

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

No annotations exist, so the description fully carries the burden. It declares the tool is a 'Pure HTML parser' (no side effects), details exactly which href types are skipped, and describes the output structure. All behavioral traits are disclosed, making the tool's behavior predictable.

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 moderately concise and front-loaded with the key phrase 'Pure HTML parser.' Every sentence provides specific information, though it could be slightly shorter without losing clarity. Structure is logical.

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

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

Given the tool's simplicity, the description explains the output thoroughly (fields returned, internal vs external, skipped links) and links to its intended downstream use (compute_link_graph). No output schema exists, but the description compensates fully.

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

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

Schema description coverage is 100%, with both parameters well-described in the schema. The description adds minimal extra meaning for parameters beyond what the schema provides (e.g., baseUrl used for resolution). Baseline of 3 is appropriate.

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

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

The description uses a specific verb ('extract') and clearly defines the resource (links from HTML). It explicitly states the output format (absolute, internal vs external, cleaned anchor text, rel, nofollow) and what is skipped (fragments, mailto:, etc.). This distinguishes it from siblings like compute_link_graph, which consumes its output.

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

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

The description explains that it is designed to feed compute_link_graph and that it should be called per page crawled from the user's machine. This gives clear context for when to use it, though it does not explicitly list scenarios where it should not be used (e.g., if no raw HTML is available).

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

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

No annotations are provided, so the description bears the full burden. It does not disclose behavioral traits beyond the purpose, such as read-only nature, caching, or rate limits. For a simple data retrieval with no parameters, this is adequate but not thorough.

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 long, front-loaded with the primary function, and contains no superfluous words. 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 no output schema, the description does not detail the return format. However, it suffices for a simple tool returning a canonical description. It could mention the format (e.g., plain text), but the use cases are well-covered.

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 no parameters, and schema description coverage is 100%. The description adds no parameter info, but none is needed, so the baseline of 4 is appropriate.

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

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

The description clearly states the tool returns the canonical AI-readable description of Momentic from /llms.txt, specifying the resource and its use for brand info, positioning, services, and contact. It distinguishes from sibling tools like analyze_page or search.

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

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

The description indicates when to use the tool ('for authoritative brand info...'), but does not explicitly mention when not to use it or provide alternatives. However, the context signals and sibling tools make it sufficiently clear.

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

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

With no annotations, the description should disclose behavioral traits; it mentions 'uses Cloudflare's Markdown for Agents' but omits details on auth, error handling, rate limits, or whether the operation is read-only.

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

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

Two sentences, front-loaded with the core function and usage, no unnecessary words or redundancy.

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

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

For a simple tool with one required parameter and no output schema, the description covers the basic purpose and parameter usage, but lacks details on return format beyond Markdown, error cases, or behavioral guarantees.

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 a single parameter described. The description reinforces the schema's explanation and adds concrete examples ('/team/jane-doe'), but does not provide significant additional 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?

The description clearly states the tool fetches a Momentic page as Markdown, specifies the verb ('Fetches'), resource ('Momentic page'), and output format, and distinguishes from siblings like analyze_page by focusing on raw content retrieval.

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

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

The description provides clear instructions on parameter format ('absolute path like "/team/jane-doe" or a full URL'), but does not explicitly state when to use this tool versus alternatives like analyze_page or search.

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?

Without annotations, the description discloses the data source (sitemap.xml) and filtering. However, it omits details like return format, pagination, rate limits, or authentication needs.

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

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

Two sentences: first states purpose and source, second explains optional parameter. No redundant words, front-loaded.

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

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

For a simple list tool with one optional param and no output schema, the description covers the core functionality and usage. Minor gap: no mention of output structure (e.g., list of URLs).

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?

Single parameter with 100% schema coverage. Description adds example values and clarifies default behavior (omit to list all), complementing the schema's generic description.

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?

Clearly states it lists pages from a specific sitemap URL, with optional section filter. Distinguishes from siblings like get_page (single page) and search (cross-site search).

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

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

Explains optional section filter and default behavior (list all). Does not explicitly state when not to use or list alternatives, but the context is clear enough for an 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?

No annotations are provided, so the description carries full burden. It discloses that the tool uses free APIs with no authentication, and explains behavior like returning alternativeMatches when a name search yields multiple candidates. It does not mention rate limits or caching, but for a read-only lookup, these are less critical.

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 fairly long but every sentence adds value. It is front-loaded with the main purpose and method of use. Minor trimming could be possible, but overall it is efficient for the amount of information conveyed.

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 that there is no output schema, the description thoroughly explains the return values including qid, label, description, instanceOf, sameAs, sitelinks, Wikipedia summary, sections, and alternativeMatches. It also covers all four parameters and provides use cases. The tool is fully specified for an AI agent to invoke correctly.

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

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

Schema description coverage is 100%, but the description adds value beyond the schema by explaining how the 'name' parameter resolves to the top match and returns alternatives, and describes the effect of skipWikipedia. This provides deeper meaning for parameter usage.

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

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

The description clearly states it resolves a brand/topic/person to a structured entity record built from Wikidata and Wikipedia. It lists the three ways to call it (name, qid, wikipediaTitle) and details the return fields. This distinguishes it from sibling tools like analyze_page or get_brand_overview.

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

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

The description explicitly states to pass exactly one of name, qid, or wikipediaTitle. It also provides context for when to use the tool (entity-graph alignment checks, content briefs). However, it does not explicitly mention when not to use this tool or suggest alternatives, which would be helpful.

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

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

No annotations are provided, so the description bears full responsibility. It fully discloses that the tool is a parser that does not fetch, lists all markers it extracts, and describes what it returns (per-source ISO dates, freshest source, cross-source spread, heuristic flags). This leaves no ambiguity about its 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 a single paragraph but packs significant information efficiently. It front-loads 'Pure parser (no fetching)' as the key caveat. Every sentence adds value; however, it could be broken into smaller sections for readability.

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 (four parameters including nested objects, no output schema), the description is remarkably complete. It explains the purpose of each parameter, enumerates all extracted markers, and describes the return value components (per-source dates, freshest source, spread, heuristic flags). It also ties the tool to a specific usage context (geo-aeo-readiness skill).

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 descriptions for all four parameters. The tool description adds context beyond the schema: url is 'for context', headers are expected with 'lowercased keys', and sitemapEntry is a 'parsed sitemap entry'. It does not fully detail the structure of the output object but compensates by listing return fields in free text.

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 it is a 'pure parser (no fetching)' and enumerates exactly which freshness markers it extracts (HTTP Last-Modified, sitemap lastmod, JSON-LD dates, etc.). It identifies the tool's resource (freshness markers in HTML) and distinguishes it from sibling tools like 'get_page' (which fetches) by emphasizing it does not fetch.

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 specifies the tool is for extracting freshness signals and notes its use in the 'geo-aeo-readiness skill's freshness check'. It implies when to use (when you need to parse freshness markers from already-fetched HTML) but does not explicitly state when not to use it or provide direct comparisons to alternatives among the 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?

No annotations, so description carries full burden. It describes the search behavior but does not mention any potential limitations like result count, caching, or whether it always queries fresh data. Adequate but not thorough.

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

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

Two sentences that efficiently convey the tool's purpose and the alternative for deeper search. No redundant information.

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

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

For a simple search tool with one parameter and no output schema, the description covers the core functionality. It could mention the result format or absence of content search, but it's mostly 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 already describes 'query' as 'Search term', but description adds specific behavior: case-insensitive substring match on URL. Adds meaningful context 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?

Clearly states it searches across page URLs from sitemap.xml with case-insensitive substring matching. Distinct from sibling tools like get_page which fetch matching pages.

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 suggests using get_page for deeper search, providing context for when to use this tool. No explicit when-not-to-use, but guidance is clear.

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