Verifies which domain officially belongs to a software product, AI tool, or company, so the agent stops handing users lookalike download sites. Ownership only, never safety. Every verdict is backed by reproducible evidence. Tools: get_official_url(name), verify_url(url). No auth required.
Look up the verified official website for a software product, AI tool, or company by name. Returns verified URLs with the evidence behind them, or says the site could not be confirmed.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Product, tool, or company name, e.g. 'Ollama', 'Claude Code' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral disclosure burden. It clearly communicates a read-only lookup behavior and discloses both success and failure outcomes (verified URLs with evidence vs. cannot confirm). It does not detail verification sources or data freshness, but that is not essential for correct invocation.
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 efficient sentences front-load the core action and immediately describe the return behavior. There is no redundant filler, and 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 single-parameter read-only lookup with no output schema, the description adequately covers what the tool does and what the result will look like. It could be slightly more explicit about the structure of the returned evidence, but that is a minor gap given the simplicity of 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 coverage is 100% and the only parameter is documented with an example. The description adds little beyond the schema, only reinforcing that the lookup is by product, tool, or company name. This matches the baseline for high schema coverage.
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 ('Look up'), a clear resource ('verified official website'), and a scope ('by name') that distinguishes it from the sibling verify_url, which is about validating an existing URL. It also states the output type: verified URLs with evidence or an unconfirmed message.
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 this tool — when you need the official website for a named product, tool, or company. However, it never explicitly contrasts this with verify_url, nor does it state conditions for when not to use it or when to prefer an alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check whether a URL or domain is the verified official website of a software product, AI tool, or company. Returns official | not_official (with the real official domains) | insufficient_evidence | unknown. Judges ownership only, never safety.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL or bare domain, e.g. https://claude-desktop.io/download |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does well: it discloses the complete output taxonomy, clarifies that not_official comes with the real official domains, and states the scope boundary (ownership only, never safety). It omits process-level details like network behavior or redirect handling, but the essential behavioral traits are covered.
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 zero waste: purpose, output values, and scope limitation. Each sentence carries distinct information and the primary purpose is front-loaded.
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 simple one-parameter classifier with no output schema, the description is nearly complete: it covers purpose, accepted input, all return categories, and what the tool intentionally does not do. The only gap is operational behavior (how verification is performed, failure modes), which is a minor omission for this tool type.
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 schema already documents the single url parameter with an example. The description adds marginal context by scoping which kinds of targets are relevant (software products, AI tools, companies) but provides no additional format guidance beyond the schema. 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 states a specific verb ('check'), a specific resource (URL or domain), and a precise purpose (is it the verified official website). The enumerated return values (official | not_official | insufficient_evidence | unknown) clearly distinguish it from the sibling get_official_url: this tool classifies a given URL rather than retrieving one.
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 core use case is implied by the purpose statement, and 'never safety' is an explicit exclusion that tells the agent not to use this tool for safety judgments. However, the description never names get_official_url or states when to prefer one tool over the other, so alternative routing is left to inference.
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.
get_official_urlverify_urlClaiming 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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Add one secure layer between your agents and this server.
The two tools have clearly distinct purposes: one performs a forward lookup by product name, the other does a reverse verification on a provided URL. There is no ambiguity between them.
Both tool names follow the same verb_noun convention with underscores, making the pattern predictable. 'get_official_url' and 'verify_url' are stylistically consistent and readable.
Two tools is on the low end of the typical range, but the domain is narrowly scoped to official URL discovery and verification. Each tool serves a necessary and complementary role, so the count feels reasonable rather than thin.
The tool surface covers both natural operations for this domain: finding the official URL for a name and verifying a given URL. The verify tool also returns evidence-based statuses, so the workflow does not dead-end.