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Pass an anew.page URL, get back the HTML document encoded in it, plus its canonical URL and byte size. (Two in-page tools share this name and take no arguments: the editor's reads the document open in the editor, and a rendered page's reads the page being viewed. This one takes a url and reads that page.) To revise an existing anew page, its URL is its source: read it, edit that HTML, write it again. The new URL is a separate page and the original keeps working. Read returns the author's exact bytes — no anew-injected tags, no proxied image srcs — so it re-encodes byte-for-byte. Never re-author from a rendered page, a scraped DOM, or a screenshot: each loses the author's markup, and none is faster than reading the slug. Decoding is local and exact, so any anew URL reads — the page needs no prior visit and no fetch.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | An anew.page URL, or a bare slug. Subdomain, cosmetic-prefix and #hash forms all resolve. | |
| format | No | How the document rides back. "text" (default) is one plain text block. "resource" is one embedded-resource block ({type:"resource"}, mimeType text/html) — the typed MCP form for callers that want the page tagged as HTML rather than loose text. Same bytes both ways. | text |
| Name | Required | Description |
|---|---|---|
| url | Yes | Canonical anew.page URL for this document. |
| bytes | Yes | Encoded URL length in bytes. |
| limit | Yes | Unfurl-safe URL threshold (4000); headroom = limit - bytes. Past it the URL still works (hard ceiling 65000), it may just break in some apps. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations are all false, so the description carries the burden of explaining behavior. It does so well: it reveals that read returns exact bytes with no injected tags or proxied image srcs, that decoding is local and exact, and that any URL can be read without prior visit or fetch. It doesn't discuss rate limits or error cases, but it goes beyond typical descriptions.
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 longer than average, but every sentence earns its place. The most important fact — what the tool returns — is front-loaded, and the caveats (exact bytes, local decoding, never re-author from rendered pages) all prevent real mistakes. No filler or repetition of schema content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a read tool with a rich input schema and output schema, this description is complete. It covers the tool's purpose, main constraints, when to use it, when not to, and the critical byte-for-byte guarantee. The output schema already documents the return shape, so the description doesn't need to explain it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds meaningful context for the url parameter ('or a bare slug', 'subdomain, cosmetic-prefix and #hash forms all resolve') and clarifies the format parameter's semantic difference ('same bytes both ways'), which is exactly what an agent needs to choose correctly.
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 action ('Pass an anew.page URL, get back the HTML document encoded in it') and names the resource and the exact output fields. It also disambiguates this tool from the two in-page tools that share the name, which is critical given the ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly says when to use this tool — to read or revise an existing page — and when not to: 'Never re-author from a rendered page, a scraped DOM, or a screenshot'. It also distinguishes the sibling anew_write by explaining that the URL is the source and editing requires read-then-write. This is strong guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Encode a self-contained HTML document (or fragment, or plain text) into an anew.page URL and return that URL. Each encoding path is deterministic: the same input through the same path always produces the same URL. Different paths use different Brotli encoders, though, so the same HTML may mint different — equally valid — URLs; compare pages by their decoded HTML, never by slug. Inline all CSS, JS, and images (data: URIs, inline SVG, CSS gradients, or emoji) — the page should render alone forever, and each external reference gambles it on someone else's host staying up. One accepted exception: a webfont is far too large to inline, so a font may load from a durable CDN with a system-stack fallback; do not fight a user who asks for one. The HTML should stay ≤ 160000 UTF-8 bytes and the encoded URL ≤ 4000 bytes — the recommended share-safe budgets, not walls: a page past either still mints, renders and serves, up to the physical ceilings (65000 URL bytes, where the edge severs the request line; 163839 decoded bytes, the server's decode cap), it may just break in some apps — the response says so with a warning. Past a ceiling the endpoint refuses with an actionable error (code url_over_budget or content_too_large). Brotli slugs (the endpoint, or a local brotli mint) put a typical page in a few hundred URL bytes; raw base64 fits only ~2900 HTML bytes share-safe — know which budget you are drafting to, and never cut page content to fit before checking the brotli path. Returns the URL as plain text — return it to the user verbatim. To revise an existing anew page, its URL is its source: read it, edit that HTML, write it again. The new URL is a separate page and the original keeps working. Read it with anew_read first, then call this with the edited HTML.
| Name | Required | Description | Default |
|---|---|---|---|
| html | Yes | A self-contained HTML document, fragment, or plain text. Encoded into the returned URL. |
| Name | Required | Description |
|---|---|---|
| url | Yes | The anew.page URL — return verbatim. |
| bytes | Yes | Encoded URL length in bytes. |
| limit | Yes | Unfurl-safe URL threshold (4000); headroom = limit - bytes. Past it the URL still works (hard ceiling 65000), it may just break in some apps. |
| warning | No | Present when the page minted past a share-safe budget (URL over the share-safe budget, or content over the recommended byte budget): the URL works and serves, with the stated caveat. Relay it to the user with the URL. |
| decoded_bytes | Yes | The submitted HTML in UTF-8 bytes. |
| decoded_limit | Yes | Recommended decoded-byte budget (160000). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations signal readOnlyHint=false, openWorldHint=true, idempotentHint=false, and destructiveHint=false. The description goes well beyond these to explain determinism per encoding path, variability across Brotli encoders, overflow behavior with warnings, error codes, and the fact that the original page keeps working after a revision. This substantially enriches the agent's behavioral model of the tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is long but densely informative; the core purpose is front-loaded in the first sentence and every subsequent clause addresses a real invocation concern. It lacks bullet-point structure and could be slightly tighter, but it earns its length by covering determinism, budgets, warnings, error codes, and the revision workflow without waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's nuance — encoding paths, size limits, self-contained content requirements, return format, and revision semantics — the description is exceptionally complete. It covers error codes, physical ceilings, and even instructs the agent to return the URL verbatim. The output schema exists, so not re-explaining return values 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?
Although schema coverage is 100%, the description adds extensive meaning to the html parameter: it must be self-contained, must inline resources, should respect budget ceilings, and can be a document, fragment, or plain text. This goes far beyond the schema's minimal description and materially helps an agent prepare valid input.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Encode a self-contained HTML document (or fragment, or plain text) into an anew.page URL and return that URL.' This clearly distinguishes the write operation from its sibling anew_read. It also immediately clarifies the input-to-output transformation, leaving no ambiguity about what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives explicit guidance on when to use this tool, including a revision workflow: 'Read it with anew_read first, then call this with the edited HTML.' It also provides actionable constraints such as inlining all CSS/JS/images, size budgets, and when a webfont CDN exception is acceptable. This tells an agent not just what the tool does, but how to invoke it correctly in context.
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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Add one secure layer between your agents and this server.
The server exposes exactly two operations with fully distinct roles: one decodes an anew URL into HTML, the other encodes HTML into an anew URL. There is no overlap or boundary ambiguity between read and write.
Both tool names follow the identical anew_<verb> pattern: anew_read and anew_write. The naming convention is perfectly consistent and immediately signals each tool's action.
The server's domain is a narrow encode/decode utility, and two tools fully cover it; each tool is essential and earns its place. The count is appropriate even though it is below the typical 3-15 range because the scope is intentionally minimal.
The read/write pair covers the full lifecycle of this URL-encoded page format, including the documented read-edit-write revision workflow. No obvious operations are missing: URLs are immutable by design, so delete is not applicable and no listing/search is needed for self-contained URLs.