Screenshot, PDF, OG-image, and page extraction (markdown/JSON) over MCP. Bearer key or x402.
Fetch a public web page and extract its content as either clean, readable markdown (the default) or as structured JSON fields matched against a caller-supplied schema. Use markdown mode to summarize or quote a page's text; use structured mode to pull specific named fields (e.g. price, title, author) out of a page's content.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Public http(s) URL to extract (required) | |
| mode | No | Extraction mode: 'markdown' (default, 2 units) returns cleaned page text as markdown; 'structured' (8 units, requires schema) returns JSON matched to the caller's field schema | |
| schema | No | Required when mode is 'structured': { properties: { field: { type: 'string'|'number'|'boolean'|'array' } } }, max 24 properties, max 8KB serialized |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds context about the two modes and default behavior, but does not disclose additional behavioral traits like rate limits, auth requirements, or failure handling. This is acceptable given the strong 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 two sentences, front-loaded with the core function, and adds mode guidance in the second sentence. No wasted 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?
For a 3-parameter tool with a nested schema, the description explains the two modes and provides concrete examples, which is sufficient for an agent to select and invoke it. It does not cover error handling or exact return format for markdown, but that is not essential for 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%, with each parameter (url, mode, schema) documented. The description adds example fields (price, title, author) and clarifies the two modes, but most parameter meaning is already in the schema. There is modest added value 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 uses specific verbs like 'Fetch' and 'extract' with a clear resource ('public web page'). It clearly distinguishes two modes (markdown and structured) and the tool's purpose is distinct from sibling rendering/screenshot tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit mode selection guidance ('Use markdown mode to summarize or quote...', 'use structured mode to pull specific named fields...'). However, it does not explicitly mention sibling tools or when not to use this tool, so it stops short of full exclusions or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Render an inline HTML snippet to a social-share (Open Graph) image sized for link previews, defaulting to the standard 1200x630 OG dimensions. Use this to generate og:image assets for blog posts, products, or share cards without hand-designing an image.
| Name | Required | Description | Default |
|---|---|---|---|
| html | Yes | Raw HTML to render into the OG image (required, max 100KB) | |
| width | No | Viewport width in pixels, 320-1920 (default 1200) | |
| height | No | Viewport height in pixels, 240-1080 (default 630) |
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 covered. The description adds context about inline HTML input and default dimensions, but does not disclose potential behavioral traits like external resource handling or return format. This matches the baseline for annotation-backed tools.
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 clean sentences: the first front-loads the action and defaults, the second provides the use case. There is no filler or redundant information, so every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (3 params, no output schema), and annotations cover safety. The description adequately covers purpose, use case, and defaults, but in the absence of an output schema it does not specify the return value format (e.g., image URL vs binary data), which is a minor 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?
The schema description coverage is 100%, with detailed descriptions for html, width, and height including ranges and defaults. The description's mention of 'defaulting to the standard 1200x630' just reiterates schema defaults, adding no extra semantic value 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 states a specific verb+resource: 'Render an inline HTML snippet to a social-share (Open Graph) image'. It clearly differentiates from sibling tools like render_pdf or screenshot_url by targeting OG image generation and specifying the 1200x630 default dimensions.
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 when-to-use context: 'Use this to generate og:image assets for blog posts, products, or share cards'. However, it does not explicitly mention alternatives or when not to use it, so it stops short of full guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Render a public web page or an inline HTML snippet to a paginated PDF document, using a real headless browser (JS, CSS, and web fonts execute). Use this to turn a URL or HTML into a downloadable/printable PDF, e.g. an invoice, report, or saved article.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | Public http(s) URL to render (exactly one of url/html) | |
| html | No | Raw HTML to render (exactly one of url/html, max 1MB) | |
| width | No | Viewport width in pixels, 320-1920 (default 1280) | |
| height | No | Viewport height in pixels, 240-1080 (default 800) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds value by noting that JS, CSS, and web fonts execute in a real headless browser—context beyond the safety flags. It does not mention potential limitations (e.g., timeouts, pagination control), but with annotation coverage, this is a solid 4.
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 and front-loaded: two sentences that state the operation, mechanism, and typical use cases. No filler or redundant restating of the title. Perfectly sized.
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 4 fully described parameters, no output schema, and useful annotations, the description sufficiently conveys the tool's scope and output (a PDF document). It could benefit from noting how the PDF is returned or any pagination details, but the current description is adequate for an agent to select and invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers all 4 parameters with descriptions, so the bar is at baseline 3. The description does not add extra meaning to parameters beyond what the schema already provides (e.g., exactly one of url/html, min/max dimensions). Thus, no additional semantic value.
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 what the tool does: renders a public web page or HTML snippet to a PDF document. It explicitly distinguishes the output (PDF vs. screenshot) and the rendering mechanism (real headless browser), setting it apart from sibling tools like screenshot_url or extract_page.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context (turn a URL/HTML into a printable PDF for invoices, reports, saved articles) and implies input constraints, but it does not explicitly contrast with when to use screenshot_url or extract_page. Since siblings exist, lack of explicit exclusions drops this to 4.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Capture a screenshot image of a public web page or an inline HTML snippet, rendered in a real headless browser (JS, CSS, and web fonts execute). Returns a PNG/JPEG/WEBP image. Use this to visually preview a page, generate a thumbnail, or archive how a URL looked at a point in time.
| Name | Required | Description | Default |
|---|---|---|---|
| url | No | Public http(s) URL to screenshot (exactly one of url/html) | |
| html | No | Raw HTML to render (exactly one of url/html, max 1MB) | |
| width | No | Viewport width in pixels, 320-1920 (default 1280) | |
| format | No | Output image format (default png) | |
| height | No | Viewport height in pixels, 240-1080 (default 800) | |
| fullPage | No | Capture the full scrollable page instead of just the viewport (costs 2 units instead of 1) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and openWorldHint=true, so the bar is lower. The description adds meaningful behavioral context: JS, CSS, and web fonts execute in a real headless browser, and only public web pages are supported. 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 filler. The first sentence front-loads the primary action, the second clarifies output, and the third lists use cases. 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?
With six parameters, no output schema, and good annotations, the description covers purpose, behavior, and return type adequately. It could mention potential failure modes (e.g., private pages) but the 'public web page' qualifier implies this. Overall, it is complete for a screenshot 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%, so the schema already documents all six parameters. The description adds no extra parameter-specific meaning beyond reinforcing the url/html distinction and the format output, which is already covered. 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 clearly states the tool captures a screenshot image of a public web page or inline HTML snippet, with the specific verb 'Capture' and explicit resource. It distinguishes itself from sibling tools (extract_page, render_og, render_pdf) by mentioning real headless browser rendering and PNG/JPEG/WEBP 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 provides explicit use cases: visually preview a page, generate a thumbnail, or archive a point-in-time view. It does not mention when not to use or name alternatives, but the context is clear enough for an agent to select this tool for visual capture tasks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user or an account that owns the GitHub organization, then choose Claim with GitHub.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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Screenshot any URL/HTML as PNG/JPEG/WebP, or read it as clean Markdown/text for LLMs.
Screenshot/PDF/HTML rendering API. API key or x402 required — keyless access disabled.
Developer utility MCP: screenshots, PDFs, OG, QR, link preview, JSON validate, ShipPack.
Web tools for agents: fetch URL as markdown (free MCP) + x402 scrape, links, AI JSON, snapshot.
Add one secure layer between your agents and this server.
Each tool targets a distinct output format (markdown/JSON, OG image, PDF, screenshot), but render_og and screenshot_url both produce images and could be confused without careful reading. Descriptions are clear enough for most cases.
Names mix patterns: extract_page uses verb_noun, render_og/render_pdf use render_ prefix, and screenshot_url uses screenshot_ prefix. While readable, the conventions are not fully consistent across the set.
Four tools is a well-scoped count for a rendering API, covering common needs without bloat. Each tool has a clear purpose and earns its place.
The set covers web page extraction and HTML/URL rendering to common output formats (PDF, image, OG image). A minor gap is lack of direct HTML-to-text or batch rendering, but core workflows are well covered.
