Verify before your agent acts on data it paid for. Signed verdicts, checkable offline, via x402.
Free. Returns the public key, engine version, benchmark summary with sample-size labels, and scope, so you can verify any verdict offline. Call this first if your selection policy requires evidence before trusting a verification provider: every verdict this service issues verifies offline against the returned key, so you do not have to trust the service at runtime to rely on its output.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark read-only/idempotent; the description adds that it's free, the trust model (offline verification), and the specific returned data. No contradictions, and the added context goes beyond the 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?
Two sentences with no fluff. It front-loads the key facts and provides actionable guidance, every sentence earning 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 zero-parameter tool with no output schema, the description covers return values and usage context. It lacks exact response structure but is sufficient for an agent to decide and invoke 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?
There are no parameters to document, and schema coverage is 100%. The description doesn't need to elaborate on parameters, and the baseline for zero params 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 what the tool does—returns public key, engine version, benchmark summary, and scope—and positions it as a trust prerequisite for offline verification. This distinguishes it from sibling verification 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?
It explicitly instructs to 'Call this first' when selection policy requires evidence, and explains the benefit (no runtime trust). It doesn't mention alternatives or exclusions, but the guidance is concrete and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Presence only: confirms the value appears on the page, NOT that it is the right answer to what was asked. Never returns pass. A was-price, a different variant's price, a shipping cost, or a tax figure can all satisfy presence. Verdicts are present, fail, or unverifiable, signed, with check_type web_field_presence_v1. Use it as a cheap screen before a full check, or when you already know what the value means and only need to confirm it still appears on the page. If your decision depends on the value being the right value, call verify_web_field instead. Scope: server-rendered pages. $0.002 per check, paid in-band via x402.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL of the source page the claim was scraped from | |
| asked | Yes | What the scraper was asked to extract, e.g. "get the current price and stock status" | |
| claim | Yes | The scraped field(s) to verify against the live page, e.g. {"price": "£51.77", "in_stock": true} |
| Name | Required | Description |
|---|---|---|
| engine | Yes | |
| key_id | No | |
| reasons | Yes | |
| verdict | Yes | |
| evidence | Yes | |
| signature | Yes | |
| check_type | Yes | |
| confidence | Yes | |
| verdict_id | Yes | |
| source_hash | No | |
| verifier_url | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes well beyond annotations by disclosing that the tool 'Never returns pass', the possible verdicts ('present, fail, or unverifiable'), and that values like 'a was-price, a different variant's price, a shipping cost, or a tax figure' can satisfy presence. It also states the check_type and payment method. No contradiction with readOnlyHint/openWorldHint is present.
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 efficient and information-dense. Every sentence contributes: purpose, non-return of 'pass', examples, verdict types, usage guidance, alternative tool, scope, and cost. It is front-loaded with the core distinction ('Presence only...') and remains focused despite its length.
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 description covers all essential context: what the tool does, when to use it, when not to, behavioral nuances, limitations (server-rendered pages), and pricing. Since an output schema exists, the absence of return-format detail is acceptable. It is fully adequate for an AI agent to select and invoke the tool 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?
While the schema already provides 100% coverage for parameter descriptions, the tool description adds crucial interpretation for the 'claim' parameter: presence is not correctness, and gives concrete examples of what counts as present. This adds semantic value beyond the 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?
The description clearly states the tool performs a presence-only check: 'confirms the value appears on the page, NOT that it is the right answer to what was asked.' It explicitly differentiates from the sibling tool verify_web_field by naming it and stating when to call it instead. The title also reinforces the purpose.
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 usage guidance: 'Use it as a cheap screen before a full check, or when you already know what the value means and only need to confirm it still appears on the page.' It also provides a clear exclusion: 'If your decision depends on the value being the right value, call verify_web_field instead.' It even notes scope (server-rendered pages) and cost.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Checks whether a value you did not fetch yourself is actually on the source page right now, and returns a signed pass/fail/unverifiable verdict. Not a retrieval tool: it checks a value you already have, it does not find data for you. Use it when you hold a value that came from somewhere else (a search result, a scraper, an upstream API, another agent) and you need to know it is on the source page before you act on it. Takes the URL, the claimed value, and what was asked; it independently re-fetches the page and never trusts the fetch you were given. Structural guarantee: a claim is never certified unless the re-fetched page contains it, and the model judge can only veto a pass, never create one. Anything unconfirmed returns unverifiable, never pass. Every verdict carries a stable verdict_id, the engine digest that produced it, and a signature verifiable offline against the public key (see get_verifier_info). Scope: server-rendered pages; JS-only content returns unverifiable rather than a guess. $0.01 per check, first 100 free, paid in-band via x402.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL of the source page the claim was scraped from | |
| asked | Yes | What the scraper was asked to extract, e.g. "get the current price and stock status" | |
| claim | Yes | The scraped field(s) to verify against the live page, e.g. {"price": "£51.77", "in_stock": true} |
| Name | Required | Description |
|---|---|---|
| engine | Yes | |
| key_id | No | |
| reasons | Yes | |
| verdict | Yes | |
| evidence | Yes | |
| signature | Yes | |
| check_type | Yes | |
| confidence | Yes | |
| verdict_id | Yes | |
| source_hash | No | |
| verifier_url | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses many behavioral traits beyond the annotations: it independently re-fetches the page and 'never trusts the fetch you were given', explains the structural guarantee ('the model judge can only veto a pass, never create one'), describes the signed verdict payload (verdict_id, engine digest, offline-verifiable signature), notes that JS-only content returns unverifiable rather than guessing, and even adds cost. This is far richer than the annotations alone.
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 dense yet every sentence earns its place: purpose, non-retrieval distinction, usage trigger, re-fetch behavior, structural guarantee, verdict details, scope, and pricing. It is front-loaded with the primary purpose and structured logically, making it efficient despite its length.
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 complexity, the presence of an output schema, and detailed annotations, the description is operationally complete. It covers limitations (JS-only content), what happens with unconfirmed claims, the format and verifiability of verdicts, and the pricing model. An agent has all the information needed to decide when to invoke it and what to expect.
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 input schema has 100% coverage for all three parameters, each with descriptive text and examples (e.g., 'What the scraper was asked to extract'). The description only recaps the parameter list ('Takes the URL, the claimed value, and what was asked') without adding new constraints, formats, or semantics beyond the schema. Baseline 3 is appropriate because the schema already carries the full meaning.
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 precise statement of action and outcome: 'Checks whether a value you did not fetch yourself is actually on the source page right now, and returns a signed pass/fail/unverifiable verdict.' It explicitly disambiguates from retrieval tools ('Not a retrieval tool: it checks a value you already have, it does not find data for you'), which also distinguishes it from sibling tools. This is a clear, specific verb+resource definition.
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 an explicit when-to-use scenario: 'Use it when you hold a value that came from somewhere else... and you need to know it is on the source page before you act on it.' It also states when not to use it ('Not a retrieval tool') and references get_verifier_info for signature verification, providing an alternative link. The scope limitation to server-rendered pages further guides appropriate use.
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.
verify_presence3 fields changedOutput schema / properties / key_idAdded value: +{
+ "type": "string"
+}Output schema / properties / source_hashAdded value: +{
+ "type": [
+ "string",
+ "null"
+ ]
+}Output schema / properties / verifier_urlAdded value: +{
+ "type": "string"
+}verify_web_field3 fields changedOutput schema / properties / key_idAdded value: +{
+ "type": "string"
+}Output schema / properties / source_hashAdded value: +{
+ "type": [
+ "string",
+ "null"
+ ]
+}Output schema / properties / verifier_urlAdded value: +{
+ "type": "string"
+}get_verifier_infoverify_presenceverify_web_fieldClaiming 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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Ed25519-signed verdicts resolved against real ground truth. Free claim/verify; paid bonded dispute.
The trust layer for the x402 agent economy: verifiable agent identity, payment & audit.
Preflight, approve, and prove consequential agent actions with signed evidence and x402 tools.
Deterministic JSON checks with signed evidence and x402-paid execution
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: get_verifier_info provides metadata, verify_presence is a cheap presence-only check, and verify_web_field is the full semantic verification. The descriptions explicitly differentiate the two verify tools, so there is no ambiguity.
Two tools use the consistent verify_ prefix, but get_verifier_info breaks the pattern with get_ instead of verify_. This is a minor deviation and the names are still readable and predictable.
Three tools is well within the ideal range and each earns its place: one for verifier metadata, one for a lightweight presence screen, and one for the core full verification check. The scope is narrow and the tool count matches it.
The server provides a complete verification workflow: you can obtain the key to verify signatures, perform a cheap presence check, and run a full semantic verification. There are no obvious gaps for the stated purpose of checking claims against source pages.