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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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4/5 across 2 of 2 tools scored.
The two tools have clearly distinct purposes: one checks for AI-readiness signals on a site, the other simulates an AI shopping agent's experience. There is no overlap or ambiguity.
Both tools follow the verb_noun pattern consistently, using lowercase with underscores (check_ai_readiness, simulate_agent_shopping). The naming is predictable and clear.
Only two tools exist for a domain that could reasonably include more (e.g., generate readiness files, fix common issues). While the two tools serve core needs, the count feels slightly thin for a comprehensive AI-readiness toolset.
The tools cover checking readiness and simulating agent behavior, but lack capabilities to act on results (e.g., suggest fixes, generate missing files). The surface has notable gaps that limit end-to-end usefulness.
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?
The description implies a read-only operation by using 'check' and outlines what it returns (score and files). However, with no annotations provided, it does not explicitly disclose side effects, authentication needs, or non-destructive nature, which would be beneficial for an AI agent.
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 concise and front-loaded, using a single paragraph that efficiently conveys purpose, output, and usage context without redundancy.
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 tool with no output schema or annotations, the description adequately covers purpose, usage, and output. It could be improved by mentioning error handling or more detailed score interpretation, but it is largely complete.
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% with a single parameter 'url' described. The description provides example format but adds little extra meaning beyond the schema. Baseline 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 identifies the tool's purpose: checking if a website is ready for AI agents, returning a score and listing exposed files. It uses specific verbs and resources, effectively distinguishing from the sibling tool 'simulate_agent_shopping'.
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 about AI readiness, visibility, or readiness for AI shopping. It does not explicitly mention when not to use or suggest alternatives, but the context signals include the sibling tool name, implying a contrast.
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?
No annotations are provided, so the description bears the full burden. It mentions the tool is a simulation and reports stuck points, but does not disclose whether it modifies any data, requires authentication, or has rate limits. The description gives adequate context for a read-like operation but could be more explicit.
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 three sentences, front-loaded with the main action. It is well-structured and informative, though slightly verbose. It earns its length by listing example tasks, making it helpful.
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 a single parameter with full schema coverage, no annotations, and no output schema, the description adequately covers the tool's purpose and the nature of its result ('first-person verdict' and 'stuck points'). It does not specify timeouts or edge cases, but completeness is high for this complexity.
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 only parameter 'url' has 100% schema coverage. The description repeats that it expects a URL but adds no new semantics beyond what the schema already provides. 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 specifies a concrete verb ('sends', 'reports') and resource ('AI shopping agent'), and lists specific shopping tasks (catalog, product, cart, returns, checkout). It clearly distinguishes from the sibling tool 'check_ai_readiness' by focusing on the agent's experiential report.
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 wants to SEE how an AI agent would experience shopping'. It does not mention when not to use or contrast with the sibling, but the use case is clearly delineated.
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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