Free URL preflight. Cited text via separate paid Stripe MPP API.
Return the machine-payable ProofFetch offer and paid HTTP route.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It only says 'Return', which hints at a read operation, but it does not disclose whether this creates or stages an offer, requires authentication or payment, has side effects, or what 'machine-payable' implies.
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?
A single sentence with an active verb and a clear target output. It is front-loaded and contains no filler.
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?
It is adequate for a parameterless tool, but with no output schema and no annotations it could describe the structure of the returned offer/route and the relationship to the preflight sibling. Terms like 'machine-payable' are left undefined.
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 tool has zero parameters and the schema is an empty object, so there are no parameter semantics to document. The baseline of 4 applies because no parameter information is needed.
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 the verb 'Return' and clearly names the resource: 'machine-payable ProofFetch offer and paid HTTP route.' It states what the tool does, though it does not explicitly contrast it with the sibling proof_fetch_preflight, so differentiation relies partly on the tool names.
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?
There is no guidance on when to use this tool versus proof_fetch_preflight, no prerequisites, and no sequencing information. An agent must infer that an offer likely comes after a preflight step.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Free public-URL fetchability, provenance, hash, and injection-indicator scan. Does not return page text.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| maxChars | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does disclose meaningful behavior: it lists what is scanned, restricts the operation to free public URLs, and explicitly states that page text is not returned. It stops short of describing error behavior or side effects, but for a read-only preflight scan this is reasonably transparent.
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?
A single, front-loaded sentence conveys the tool's purpose and a key exclusion with no filler. Every word 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 two-parameter tool with no output schema, the description covers the main purpose and output categories but omits parameter semantics for maxChars and usage guidance relative to the sibling tool. It is serviceable but not 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 0%, so the description must compensate. It gives context for the URL parameter ('free public URL'), but says nothing about maxChars, which the schema leaves undefined beyond type and bounds. This is a significant gap for a tool with only two parameters.
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 ('scan') and identifies both the resource type (free public URLs) and the specific checks performed (fetchability, provenance, hash, injection indicators). The closing 'Does not return page text' also helps separate it from the sibling proof_fetch_offer, though the sibling is not named.
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?
There is no explicit statement of when to choose this tool over proof_fetch_offer. The only hint is the negative 'Does not return page text,' which implies the alternate tool may return page text, but no positive use case, prerequisite, or explicit alternative is provided.
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.
proof_fetch_offerproof_fetch_preflightClaiming 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, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also 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.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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Fetch a URL and get clean Markdown with metadata. No API key required; rate-limited per IP.
Link-preview metadata and clean page-to-Markdown for any public URL. No install.
Zero-signup anonymous link-unfurl API. One GET returns clean JSON metadata for any public URL.
Same link preview data as link-preview-api, ~1/3 the price via a shared cache.
Add one secure layer between your agents and this server.
The two tools have clearly distinct roles: one provides the paid offer and route, the other performs a free preflight scan. There is no possible confusion between them.
Both tools share the 'proof_fetch_' prefix and use a clear noun/verb suffix ('offer', 'preflight'). The naming pattern is uniform and predictable.
With only two tools, the server feels thin for its 'Cited Web Evidence' purpose. The count is borderline minimal and only marginally justifiable for a simple free-then-paid workflow.
The tools cover a coherent flow: preflight validation and then an offer for paid retrieval. A minor gap exists because there is no in-tool method to retrieve the actual page content, but the offer returns the route for that.
