Handiworks
Server Details
MCP tools for AI agents: render URLs to image/PDF, check link health, convert HTML/CSV/JSON.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Available Tools
3 toolscheck_linkCheck link healthAInspect
Check a URL's health: follows the redirect chain, reports the final status and URL, extracts the page , and flags likely paywalls. Cached for 6h.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The absolute URL to check. | |
| fresh | No | Bypass the 6h cache and re-check now. | |
| maxRedirects | No | Maximum redirect hops to follow. | |
| followRedirects | No | Follow 3xx redirects to the final destination. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses redirect following, cache duration, and extraction of title/paywall flags. No annotations provided, but description covers key behaviors. Could mention failure handling.
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 main action, no wasted words.
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?
No output schema, but description hints at return values (status, URL, title, paywall flag). Low complexity, complete for purpose.
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 100% of parameters. Description adds context about what the tool does with the URL (redirect chain, extraction), enriching beyond 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?
Description clearly states it checks URL health, follows redirects, reports status, extracts title, and flags paywalls. It distinguishes from siblings 'convert' and 'render' which are unrelated.
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?
Clear context: used for checking URLs. No explicit when-not-to-use or alternatives, but the purpose is self-evident.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
convertConvert content between formatsAInspect
Convert content between formats. Modes: html-to-pdf, html-to-png (real browser), csv-to-json, json-to-csv. PDF/PNG return as attachment/image; data modes return text.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | Yes | Conversion to perform. | |
| content | Yes | The input content: HTML, CSV, or JSON depending on mode. | |
| headers | No | For csv-to-json: treat the first row as column headers. | |
| delimiter | No | Field delimiter for CSV modes. | , |
| pdfFormat | No | Paper size for html-to-pdf. | A4 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses that PDF/PNG returns as attachment/image and data modes return text, which is helpful. However, it does not mention idempotency, side effects, or authorization requirements, leaving behavioral gaps.
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 with no wasted words. First sentence states purpose, second lists modes and output characteristics. Information is front-loaded and efficiently structured.
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 conversion tool with no output schema, description covers return types for all modes. Parameters are fully described in schema. Missing error handling or prerequisites, but overall provides sufficient context for agent to use 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 schema already documents parameters. Description adds value by explaining return types per mode category (attachment/image vs text) and summarizing mode behaviors, which aids agent understanding beyond schema definitions.
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 'Convert content between formats' and enumerates specific modes (html-to-pdf, html-to-png, csv-to-json, json-to-csv), making the resource and action explicit. It distinguishes from siblings check_link and render by focusing on format conversion.
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 guidance on when to use this tool versus alternatives or when not to use it. Does not mention prerequisites or context for each mode. The list of modes implies usage but lacks explicit when/when-not conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
renderRender URL to screenshot or PDFAInspect
Render a web page to a PNG/JPEG screenshot or a PDF using a real headless browser. Returns the image inline (base64) or the PDF as an embedded resource. Results are cached for 24h.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The absolute URL to render (must include https:// or http://). | |
| fresh | No | Bypass the 24h cache and force a fresh render. | |
| width | No | Viewport width in pixels. | |
| format | No | Output format. png/jpeg return an image; pdf returns a PDF document. | png |
| height | No | Viewport height in pixels. | |
| fullPage | No | For screenshots: capture the entire scrollable page instead of just the viewport. | |
| pdfFormat | No | Paper size, used only when format is pdf. | A4 |
| waitUntil | No | Navigation completion signal before capturing. | networkidle2 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses key behaviors: uses real headless browser, returns inline base64 or embedded resource, and 24-hour caching. Does not mention error handling, rate limits, or authorization needs, but covers major operational aspects.
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 action and output. Every sentence adds value. 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?
With 8 parameters and no output schema, description provides return format (base64/embedded) and caching. Could mention error conditions or typical response size, but sufficient for most use cases.
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 each parameter already has a description. The tool description adds only general context (caching, output format) without enhancing specific parameter meaning. Meets 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?
Description clearly states rendering a web page to screenshot or PDF, using a headless browser. It distinguishes from siblings 'check_link' and 'convert' by focusing on visual capture rather than link checking or format conversion.
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 (e.g., 'check_link' for link validation, 'convert' for format transformation). The description implies usage for capturing web page visuals, but lacks exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
3 tool updates
- First observed
check_link - First observed
convert - First observed
render
Frequently Asked Questions
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io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such asio.github.acme/serveralso needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at
/.well-known/glama.jsonon 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.
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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
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Credentials required to access the server are missing or invalid
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Glama MCP Gateway
Add one secure layer between your agents and this server.
TDQS
Each tool serves a distinct purpose: check_link checks URL health, convert transforms content formats, render creates screenshots/PDFs. No overlap in functionality.
Two tools use verb_noun pattern (check_link, render) while one is a single verb (convert). Mostly consistent but minor deviation.
With only 3 tools, the server is small but usable. It covers basic web utilities, though additional tools like extract_text could enhance scope.
The tool set covers link checking, format conversion, and web rendering. No obvious dead ends for these operations, though some related tasks (e.g., validate HTML) are absent.