BridgeToAgent — AI Readiness
Server Details
Is a website ready for AI shopping agents? Readiness score (0-100) + agent shopping simulation.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.3/5 across 2 of 2 tools scored.
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.
Both tools follow the same verb_noun snake_case pattern ('check_ai_readiness', 'simulate_agent_shopping'), providing a predictable naming convention.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The store's URL or domain, e.g. 'example.com' or 'https://example.com'. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, indicating a safe, externally-facing operation. The description adds that the tool returns a score and list of files, which is consistent. It does not contradict annotations and provides useful behavioral context.
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, no wasted words. The first sentence explains what the tool does and its output, the second gives usage guidance. Information is front-loaded and every sentence serves a purpose.
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, read-only tool with one parameter and no output schema, the description covers the input, output, use case, and behavioral context adequately. There are no gaps.
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 100% for the single parameter 'url'. The description adds context that the URL is for a website or online store, which enhances meaning beyond the schema's minimal description.
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 ('check') and clearly identifies the resource ('website or online store's AI readiness'). It distinguishes from the sibling tool 'simulate_agent_shopping' by focusing on readiness assessment 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool: 'when a user asks if their store/site is AI-ready, visible to AI, or ready for AI shopping'. It does not explicitly mention when not to use it or name alternatives, but the sibling tool name implies the alternative for simulation.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The store's URL or domain, e.g. 'example.com' or 'https://example.com'. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint and openWorldHint; the description adds that it simulates browsing and reports verdicts, clarifying it is non-destructive and observational.
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 concise sentences, with the core purpose front-loaded and no wasted words. Every sentence adds value.
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 simulation tool with annotations and no output schema, the description fully conveys what the tool does, what it returns, and typical tasks.
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?
Only one parameter (url) with schema description already clear; description adds no further parameter info but schema coverage is 100%, so baseline score of 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 clearly states the verb 'simulate' and the resource 'store', listing specific shopping tasks. It distinguishes from sibling 'check_ai_readiness' by focusing on experiential simulation.
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?
Explicit usage context is provided ('Use this when a user wants to SEE how an AI agent would experience...'), but it does not mention when not to use or compare to alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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