Web tools for AI agents: scrape pages to Markdown, audit SEO, detect tech stacks, check sitemaps
Each tool has a distinct primary purpose, but tooltrace_extract overlaps with links, metadata, and schema by returning all of those in addition to content. The specialized tools are still clearly differentiated, and the descriptions note the lightweight alternatives.
All tools share the consistent tooltrace_ prefix, making the family recognizable. However, the second part mixes a verb (extract) with nouns (links, metadata, schema, sitemap, tech_stack) and a hyphenated compound (seo_audit), so the pattern is not perfectly uniform.
Seven tools is well within the ideal range and each tool covers a distinct aspect of webpage analysis, from content extraction to SEO auditing and tech-stack detection. There is no bloat or obvious redundancy.
The set covers a complete workflow for analyzing web pages: full extraction, focused link/meta/schema extraction, SEO auditing, sitemap inspection, and technology detection. No critical gaps are apparent for the domain it targets.
Extract clean content from a webpage. Returns Markdown, plain text, metadata, links, JSON-LD schema, and content sections. Use this for scraping, RAG ingestion, or content analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Public webpage URL to extract | |
| mode | No | 'structured' for parsed data, 'raw' for HTML, 'both' for everything. | structured |
| render | No | Rendering mode. 'never' = fast static fetch (1 credit). 'auto' = static first, browser if needed. 'always' = browser rendering (5 credits). | auto |
| include | No | Which fields to include in the response. | |
| wait_until | No | Browser navigation milestone. Only used with browser rendering. | |
| wait_for_selector | No | CSS selector to wait for on rendered pages. |
| Name | Required | Description |
|---|---|---|
| mode | No | |
| text | No | |
| fetch | No | |
| links | No | |
| schema | No | |
| markdown | No | |
| metadata | No | |
| raw_html | No | |
| sections | No | |
| word_count | No | |
| content_hash | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already indicate readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds useful behavioral context by explaining that it returns clean, structured content in multiple formats and that it targets general extraction use cases. It does not contradict the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences with no filler. It front-loads the core action and resource, then states the outputs and use cases. Every sentence adds useful information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The input schema is rich and fully covers parameter semantics, the annotations cover safety and idempotency, and an output schema exists. The description adds the missing usage context and high-level output expectations. It is slightly incomplete in not guiding the agent toward sibling tools for narrower extraction needs, but overall it is sufficient for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and the parameters already have detailed descriptions including enums, defaults, and credit implications. The tool description does not add parameter-level meaning beyond what the schema provides, so it meets the baseline without exceeding it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and resource ('Extract clean content from a webpage') and lists the expected outputs (Markdown, metadata, links, schema, sections), which makes the tool's purpose clear. It does not explicitly distinguish this comprehensive extractor from the sibling tools like tooltrace_links or tooltrace_schema, but the broad output list implies it is the general-purpose option.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool: 'Use this for scraping, RAG ingestion, or content analysis.' However, it does not mention when not to use it or point to the more specialized sibling tools for link-only, metadata-only, or schema-only extraction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract all links from a webpage with anchor text, internal/external classification, and normalized URLs.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Public webpage URL | |
| mode | No | 'normalized' deduplicates and cleans URLs. 'raw' preserves originals. | normalized |
| render | No | Rendering mode. 'never' = fast static fetch (1 credit). 'auto' = static first, browser if needed. 'always' = browser rendering (5 credits). | auto |
| wait_until | No | Browser navigation milestone. Only used with browser rendering. | |
| wait_for_selector | No | CSS selector to wait for on rendered pages. |
| Name | Required | Description |
|---|---|---|
| mode | No | |
| count | No | |
| fetch | No | |
| links | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, open-world, non-destructive behavior, and the description does not contradict them. It adds useful operational context: it says the tool returns anchor text, classifies internal vs external links, and normalizes URLs, which tells an agent what to expect from the call.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One focused sentence with the core action front-loaded, followed by three concrete output details. No filler, no repetition of schema content, and no unnecessary clauses.
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?
Because the schema covers all parameters with defaults and enums, annotations cover safety, and an output schema exists, the description is almost sufficient. It loses one point because it doesn't orient the agent among the six sibling tools, so a caller must infer which extraction tool is appropriate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% and every parameter has a meaningful description, so the baseline is 3. The tool description adds no parameter-level details beyond saying the resulting URLs are normalized, but the schema already documents all five parameters including enums and credits.
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—'Extract all links from a webpage'—and enumerates the output: anchor text, internal/external classification, and normalized URLs. It is clearly about link extraction, though it does not explicitly contrast itself with the similarly named tooltrace_extract sibling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use it (when a caller needs page links and their metadata) but gives no exclusions, prerequisites, or alternatives. An agent must infer the boundary against sibling tools like tooltrace_extract and tooltrace_seo_audit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract page metadata: title, description, canonical URL, author, publication date, favicon, Open Graph, and Twitter card fields. Lightweight alternative to full extraction.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Public webpage URL | |
| render | No | Rendering mode. 'never' = fast static fetch (1 credit). 'auto' = static first, browser if needed. 'always' = browser rendering (5 credits). | auto |
| wait_until | No | Browser navigation milestone. Only used with browser rendering. | |
| wait_for_selector | No | CSS selector to wait for on rendered pages. |
| Name | Required | Description |
|---|---|---|
| fetch | No | |
| metadata | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds the lightweight positioning and field list, but does not disclose additional behavioral details such as rendering fallback behavior or cost implications, which are left to the 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?
Two concise sentences with no redundancy. The core purpose and field list are front-loaded, and the differential 'Lightweight alternative' earns its place without clutter.
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 input schema (100% coverage), explicit annotations, and an existing output schema, the description provides enough contextual information for an agent to select and invoke the tool correctly. 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%, with each parameter (url, render, wait_until, wait_for_selector) already having a substantive description. The tool description itself adds no parameter-level information, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Extract') and resource ('page metadata'), enumerates the exact fields returned, and explicitly positions itself as a lightweight alternative to full extraction, which distinguishes it from the likely sibling tooltrace_extract.
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 phrase 'Lightweight alternative to full extraction' gives clear contextual guidance on when to prefer this tool over the fuller extraction sibling. It does not enumerate exclusions for other sibling tools, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract JSON-LD structured data from a webpage. Returns schema.org entities like Article, Product, Organization, FAQ, BreadcrumbList, etc.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Public webpage URL | |
| mode | No | 'normalized' deduplicates entities. 'raw' preserves original JSON-LD blocks. | normalized |
| render | No | Rendering mode. 'never' = fast static fetch (1 credit). 'auto' = static first, browser if needed. 'always' = browser rendering (5 credits). | auto |
| wait_until | No | Browser navigation milestone. Only used with browser rendering. | |
| wait_for_selector | No | CSS selector to wait for on rendered pages. |
| Name | Required | Description |
|---|---|---|
| mode | No | |
| count | No | |
| fetch | No | |
| schema | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already cover read-only, idempotent, open-world, and non-destructive behavior, so the bar for additional behavioral disclosure is lower. The description adds that the tool returns schema.org entities, which is useful output context, but it does not mention edge cases like missing JSON-LD or the effect of rendering modes. 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 two concise sentences with no filler. The first sentence states the core action, and the second provides concrete examples of returned entity types. 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 rich input schema (all 5 parameters described with enums), the existing output schema, and comprehensive annotations, the description is sufficient for an agent to understand the tool's role. It does not need to explain return values or parameter details because those are already fully specified. A small enhancement would be a note about when JSON-LD might be absent, but the overall context is 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%, meaning every parameter already has a clear description in the schema. The tool description itself adds no parameter-specific meaning beyond the overall purpose, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Extract') and a specific resource ('JSON-LD structured data from a webpage'), and it includes useful examples of schema.org entity types like Article, Product, and FAQ. It clearly communicates the tool's function, though it does not explicitly differentiate itself from sibling tooltrace_extract, which could also involve extraction.
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 usage context is implied: an agent can infer it should use this tool when it needs JSON-LD/schema.org structured data from a webpage. However, the description gives no explicit when-to-use guidance, no exclusions, and no mention of alternatives among the sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run an SEO audit on a webpage. Returns a score (0-100), weighted checks for metadata, headings, images, canonical signals, robots directives, social tags, schema, and content length, with evidence and recommendations.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Public webpage URL to audit | |
| render | No | Rendering mode. 'never' = fast static fetch (1 credit). 'auto' = static first, browser if needed. 'always' = browser rendering (5 credits). | auto |
| wait_until | No | Browser navigation milestone. Only used with browser rendering. | |
| wait_for_selector | No | CSS selector to wait for on rendered pages. |
| Name | Required | Description |
|---|---|---|
| fetch | No | |
| score | No | |
| checks | No | |
| metadata | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already claim readOnly, openWorld, idempotent, and non-destructive behavior. The description adds useful behavioral detail beyond that: it returns a weighted 0-100 score, covers multiple SEO signal categories, and includes evidence and recommendations. It does not discuss rendering/credit trade-offs, but those are captured in parameter descriptions, so the gap is minor.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no filler. The first sentence front-loads the action, and the second packs the output details and audit scope into a compact list. Every clause earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given complete parameter descriptions, rich annotations, and an output schema, the description covers the core purpose and result format well. It does not explicitly position the tool against siblings or mention single-page scope, but the phrase 'a webpage' and the sibling list keep this gap minor.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%: each parameter, including url, render, wait_until, and wait_for_selector, has a meaningful description covering format, enums, defaults, and credit implications. The tool description itself does not add parameter-level detail, 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 states a specific verb ('Run an SEO audit') and resource ('a webpage'), and enumerates the checked dimensions (metadata, headings, images, canonical signals, robots directives, social tags, schema, content length). This clearly distinguishes it from sibling single-purpose tools like tooltrace_metadata or tooltrace_links, and it also specifies the output (score 0-100, evidence, recommendations).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies use when a comprehensive on-page SEO evaluation with a score and recommendations is needed, as opposed to extracting individual components with sibling tools. However, it does not explicitly name alternatives or state when not to use this tool, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Inspect a website's sitemap. Discovers sitemap URLs, parses sitemap XML, and returns listed page URLs with last-modified dates and change frequencies.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Website URL or direct sitemap URL | |
| render | No | Rendering mode. 'never' = fast static fetch (1 credit). 'auto' = static first, browser if needed. 'always' = browser rendering (5 credits). | auto |
| Name | Required | Description |
|---|---|---|
| urls | No | |
| summary | No | |
| findings | No | |
| documents | No | |
| limit_reason | No | |
| requested_url | No | |
| urls_included | No | |
| inspected_urls | No | |
| discovered_urls | No | |
| discovery_method | No | |
| inspection_complete | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, openWorld, and non-destructive behavior. The description adds useful behavioral context by explaining that the tool discovers sitemap URLs, parses sitemap XML, and returns last-modified and change-frequency data. It does not contradict the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tight sentences: the first states the high-level purpose, the second details what the tool discovers and returns. There is no filler, repetition, or unnecessary background.
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 backed by rich annotations, a 100%-described input schema, and an output schema, so the call contract is well specified. The description provides the remaining semantic context about sitemap-specific returns, making the definition complete for an agent selecting and invoking the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%: both `url` and `render` have meaningful descriptions, including rendering modes, defaults, and credit costs. The description does not add parameter-specific detail, but it does not need to because the schema already carries that 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 states a specific verb ('Inspect'), a concrete resource ('website's sitemap'), and spells out the actions and return values: discover sitemap URLs, parse sitemap XML, and return page URLs with last-modified dates and change frequencies. This makes it clearly distinguishable from sibling tools like tooltrace_links or tooltrace_metadata.
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?
Usage is implied by the name and sitemap-focused description, but there is no explicit guidance about when to use this tool versus sibling tools. No alternatives, exclusions, or conditions are mentioned, so the agent has to infer when this is the right choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Detect the technology stack of a website: CMS, frameworks, JavaScript libraries, analytics, CDN, hosting, fonts, security tools, and more. Returns categorized detections with confidence levels.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Public webpage URL to analyze | |
| render | No | Rendering mode. 'never' = fast static fetch (1 credit). 'auto' = static first, browser if needed. 'always' = browser rendering (5 credits). | auto |
| wait_until | No | Browser navigation milestone. Only used with browser rendering. | |
| wait_for_selector | No | CSS selector to wait for on rendered pages. |
| Name | Required | Description |
|---|---|---|
| url | No | |
| fetch | No | |
| domain | No | |
| categories | No | |
| technologies_detected | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, non-destructive behavior, so the description only needs to add non-safety behavioral context. It does this by stating that results are returned as categorized detections with confidence levels, giving an agent a clear expectation of the response shape. It does not discuss costs or rendering behavior, but the schema already covers credit differences for render modes.
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, front-loaded sentence that states the core action, gives a representative category list, and closes with the expected output format. There is no filler, repetition of the tool name, or restatement 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 100% schema coverage, strong safety annotations, and the presence of an output schema, the description covers the remaining need: what domain the tool operates on and what kind of result it produces. Nothing essential for selecting or invoking the tool 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 all parameters are fully documented with names, types, enums, defaults, and credit implications. The description adds no parameter-level semantics beyond what the schema already provides, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb-resource pair — 'Detect the technology stack of a website' — and enumerates concrete detection categories such as CMS, frameworks, JavaScript libraries, analytics, CDN, hosting, fonts, and security tools. This makes it immediately distinguishable from siblings like tooltrace_metadata or tooltrace_links without needing to inspect 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 usage context is implied: an agent should call this when it needs to identify a site's underlying technologies. However, the description never states when not to use it, names sibling alternatives, or addresses overlap with tooltrace_extract or tooltrace_metadata, so the guidance is only implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
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