F
licenseNot graded
qualityC
maintenanceEnables querying how often a brand is recommended by AI search and chat surfaces, returning recommendation and inclusion rates for any given brand.
Asked Thrice
Server Details
Measure whether AI assistants actually recommend a brand, from measured answers.
If you are the author of this connector, you can claim ownership with GitHub, an HTTP challenge, or a DNS record. Claimed connector authors can inspect health checks, view analytics, and manage their listing.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- nahuelsoria/llmaudit-mcp
- GitHub Stars
- 1
- Server Listing
- askedthrice-mcp
Available Tools
2 toolsget_visibility_checkGet visibility checkARead-onlyIdempotentInspect
Collect the result of a measurement started with start_visibility_check. Returns status running while the providers are still answering, so poll every 20 to 30 seconds. When ready it reports a verdict, how many of the buyer questions the brand won, which competitors were named instead, and which providers actually answered.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| runId | Yes | From start_visibility_check |
TDQS
A4.5/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnly/idempotent annotations, the description discloses important behavior: it can return a 'running' status, it requires polling, and it details what the ready result contains (verdict, won counts, competitors, providers). This gives the agent a reliable model of the tool's dynamic behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with no wasted words: purpose, polling behavior, and ready-state output are each addressed succinctly. The most important usage constraint is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter polling tool with rich annotations, the description covers the full call cycle: when to call, how long to wait, what status to expect, and what the final result contains. No output schema exists, but the description adequately compensates by listing the result components.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already documents runId with 100% coverage ('From start_visibility_check'), and the description reinforces the relationship without adding deep parameter-level detail. Baseline 3 is appropriate because the schema carries the parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the action as collecting the result of a measurement started with start_visibility_check, and it naturally distinguishes this tool from its sibling, which initiates the measurement. The verb 'collect' plus the specific resource make the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides concrete usage context: it must be called after start_visibility_check, and it should be polled every 20 to 30 seconds while status is running. It does not explicitly list exclusions or alternatives, but the single sibling relationship is clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
start_visibility_checkStart visibility checkAInspect
Measure whether a brand shows up when buyers ask AI assistants for a recommendation. Asks real buyer questions to OpenAI, Anthropic and Gemini and reports what they answered, not what they claim they would answer. Free, no signup, one measurement per domain every 30 days. Returns a runId: call get_visibility_check with it after about a minute. If the domain was already measured this month the response says quota_reached and includes when it ran.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| brand | Yes | Brand name as customers write it | |
| website | Yes | Domain, like yourbrand.com | |
| category | Yes | What the business sells, in plain words | |
| language | No | Language the buyer questions are asked in | |
| location | No | Where the business competes, like 'Cordoba, Argentina' | |
| competitors | No | Up to 5 real competitors, comma separated |
TDQS
A4.7/5.0
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes beyond the annotations by disclosing that the tool makes live calls to OpenAI, Anthropic, and Gemini, reports actual answers rather than claimed behavior, is rate-limited per domain, and returns either a runId or a quota_reached signal. It also implies the operation is asynchronous, which is important for invocation behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Four sentences, each carrying necessary information: the core measurement, the method and truthfulness, the follow-up flow, and the quota behavior. It is front-loaded with the main purpose and contains no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there is no output schema, the description adequately explains the return behavior (runId and quota_reached) and the follow-up call. It covers external side effects, constraints, and async timing, so an agent has enough context to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already documents all six parameters with descriptions, achieving 100% coverage, so the description doesn't need to repeat them. The description adds no parameter-specific guidance beyond the general mention of 'domain' in the quota rule, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear, specific objective: measure whether a brand appears in AI assistant recommendations by asking real buyer questions to OpenAI, Anthropic, and Gemini. It distinguishes itself from the sibling get_visibility_check by noting this tool starts a run and returns a runId, while the sibling retrieves the result.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides concrete usage guidance: call this to start a measurement, then call get_visibility_check with the runId after about a minute. It also states constraints (free, no signup, one measurement per domain every 30 days) and the quota_reached response, so an agent knows when this tool is or isn't available.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Frequently Asked Questions
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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.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_..."
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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:
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TDQS
A4.7/5.0
Disambiguation5/5
start_visibility_check initiates the measurement and returns a runId, while get_visibility_check consumes that runId to fetch the result. Their roles are completely distinct and easy for an agent to separate.
Naming Consistency5/5
Both tools follow a clear verb_noun pattern, sharing the same visibility_check noun and differing only in the start/get action. This is highly consistent and predictable.
Tool Count4/5
Two tools is minimal, but it fits the server's narrow start-and-retrieve workflow perfectly. No extra tools are needed for this domain.
Completeness5/5
The tool surface covers the full lifecycle of a visibility measurement: launch the check, retrieve the verdict, and handle quota scenarios through the response. There are no obvious dead ends for the stated purpose.