A
licenseB
qualityA
maintenanceMCP server for auditing product pages to determine AI shopping agent readiness, providing a 0–100 score and actionable fixes.
11
5
Apache 2.0
BridgeToAgent — AI Readiness
Server Details
Is a website ready for AI shopping agents? Readiness score (0-100) + agent shopping simulation.
- Status
- Unhealthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
MCP client
Glama
MCP server
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
100% free. Your data is private.
Tool Definition Quality
A4.4/5.0
Tool DescriptionsA
Average 4.3/5 across 2 of 2 tools scored.
Server CoherenceA
Disambiguation5/5
The two tools have clearly distinct purposes: one assesses AI readiness through file detection and scoring, the other simulates an AI agent's shopping experience. No overlap in functionality.
Naming Consistency5/5
Both tools follow the same verb_noun snake_case pattern ('check_ai_readiness', 'simulate_agent_shopping'), providing a predictable naming convention.
Tool Count4/5
With only two tools, the set is slightly sparse but still appropriate for the focused domain of AI readiness assessment. The tools cover the core use cases without unnecessary bloat.
Completeness4/5
The tool surface covers the primary actions of checking readiness and simulating shopping. However, there are minor gaps, such as the absence of tools for generating improvement suggestions or managing multiple sites.
Available Tools
2 toolscheck_ai_readinessCheck AI readinessARead-onlyInspect
Check whether a website or online store is ready for AI agents — whether assistants like ChatGPT, Claude, and Perplexity can read it, recommend it, and act on it. Returns an AI-readiness score (0–100) and which agent-readiness files the site exposes (agents.json, llms.txt, agent-instructions.md, structured data). Use this when a user asks if their store/site is AI-ready, visible to AI, or ready for AI shopping.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The store's URL or domain, e.g. 'example.com' or 'https://example.com'. |
Tool Definition Quality
A4.5/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds value by specifying the return format (score 0-100 and list of files) beyond what annotations provide. No contradictions.
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?
Two sentences efficiently convey purpose, output, and usage. No wasted words. Front-loaded with the most important information.
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 the simple single-parameter schema, annotations, and no output schema, the description fully explains what the tool returns (score and files) and when to use it. There are no gaps for the agent.
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?
Schema coverage is 100% (one required parameter 'url' with a clear description). The tool description does not add additional parameter meaning beyond the schema, so baseline 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 clearly states the verb 'check', the resource 'website or online store', and the specific focus on AI agent readiness. It lists concrete outputs (score and files) and differentiates itself from the sibling tool simulate_agent_shopping by focusing on readiness rather than simulation.
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?
The description provides explicit usage context: 'Use this when a user asks if their store/site is AI-ready, visible to AI, or ready for AI shopping.' This clearly tells agents when to invoke this tool over alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
simulate_agent_shoppingSimulate an AI agent shopping the storeARead-onlyInspect
Send an AI shopping agent at a store and report, task by task, what it can and can't do autonomously — understand the catalog, find a product, add to cart, find the return policy, complete checkout — grounded in the real signals the site exposes. Returns the agent's first-person verdict and where it gets stuck. Use this when a user wants to SEE how an AI agent would experience shopping their (or any) store.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The store's URL or domain, e.g. 'example.com' or 'https://example.com'. |
Tool Definition Quality
A4/5.0
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so agent knows no modifications and open world. Description adds context about task-by-task reporting and grounding in real signals, but no further behavioral traits like rate limits or auth needs.
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?
Two sentences, no wasted words. First sentence explains functionality, second gives usage guidance. Front-loaded with key action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (simulating an agent across multiple shopping tasks) and single parameter schema, the description adequately covers what the tool does and when to use it. However, it lacks details on return value format or limitations.
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?
Schema description coverage is 100% for the single URL parameter, so the description does not need to add more. It adds no extra meaning beyond what schema provides. Baseline 3.
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?
Clearly describes the tool's function: sending an AI shopping agent to a store and reporting what it can and can't do autonomously across shopping tasks. Specific verb+resource ('simulate', 'agent shopping') and distinguishes from sibling 'check_ai_readiness' by focusing on shopping autonomy.
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?
States when to use: when a user wants to see how an AI agent experiences shopping. Implicitly exludes other uses, but does not explicitly mention alternatives or when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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
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.
Discussions
No comments yet. Be the first to start the discussion!
Related MCP Servers
- Alicense-qualityBmaintenanceEnables AI agents to check whether AI assistants recommend a brand and audit a site's AI-agent readiness, providing visibility scores and specific gaps.MIT
ASO Score MCPofficial
AlicenseAqualityAmaintenanceScans websites to evaluate agent-readiness and produce an ASO Score Report across 34 signals, helping improve discoverability, trust, and interoperability for AI agents.534MIT- AlicenseAqualityAmaintenanceScans any website to generate an Agent Readiness Report based on the ASO framework, evaluating agent discoverability, trust, interoperability, and commerce readiness.529MIT