radveo
Server Details
Checks if AI assistants name a local business. Free shareable report, honest fixes, no guarantees.
- 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.
Each tool has a clear, distinct purpose: one checks current visibility and provides a report, the other gives a general playbook. No overlap.
Naming is inconsistent: 'check_ai_visibility' follows verb_noun pattern, while 'how_to_appear_in_ai_search' is a phrase, breaking the convention.
With only 2 tools, the server feels thin for its domain. While focused, additional tools like 'get_report' or 'list_fixes' could be expected.
The two tools cover the core needs: diagnosing current visibility and providing guidance. Minor gaps exist (e.g., no tool to implement changes), but the surface is largely complete for the stated purpose.
Available Tools
2 toolscheck_ai_visibilityAInspect
Check whether a local business currently appears when people ask AI engines (ChatGPT, Gemini, Google AI) to recommend a business in its category — and get a free, shareable Radveo report with the exact fixes to improve its odds of being named. Honest answer-engine optimization: improves the odds of being cited, never guarantees a placement.
| Name | Required | Description | Default |
|---|---|---|---|
| website | No | The business website URL, e.g. https://joesplumbing.com. Optional if business_name is given; improves accuracy. | |
| location | No | City or region, e.g. "Seattle, WA". Optional but improves accuracy. | |
| business_name | No | The business name, e.g. "Joe's Plumbing". Provide this OR a website. |
Output Schema
| Name | Required | Description |
|---|---|---|
| ok | Yes | False when the check could not be completed (bad input, rate limit, or the audit engine timed out). |
| note | Yes | Honesty framing: results improve odds and show what AI says today — never a guarantee of placement. |
| verdict | Yes | Whether AI engines named this business when asked customer-style recommendation questions. 'named' = at least one query named it; 'absent' = confidently not named; 'unknown' = the engines could not give a confident read; 'not_checked' = the live AI-answer probe did not run for this call (the on-page audit still did). |
| business | No | The business name or website that was checked. |
| seoGrade | No | Letter grade (A-D) for the readiness score, when measured. |
| seoScore | No | 0-100 on-page AI-visibility readiness score, when measured. |
| topFixes | No | The top fixes to improve the odds of being named (max 3 here; full list in the report). |
| reportUrl | Yes | Free, shareable, public report URL for this check. |
| enginesChecked | No | The AI engine panel the probes consult — an indicative read of how AI assistants tend to answer, not a live probe of every public engine. |
| queriesChecked | No | The customer-style questions asked to the AI engines (empty when the probe didn't run). |
| competitorsNamed | No | Businesses surfaced instead of this one (deduped). Same list the public shareable report shows. |
| aiVisibilityScore | No | 0-100: the share of customer-style AI queries that named the business. Null when the probe didn't produce a measured score. |
| competitorsSource | No | 'ai' = an AI engine named them; 'places' = real local leaders from Google's local results (shown when the engines named none). Null when no competitors surfaced. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the tool checks multiple AI engines, produces a free shareable report with fixes, and honestly states it never guarantees placement. It doesn't detail data handling or rate limits, but the core behavior is transparent for a read-only checking tool.
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 a single paragraph of moderate length, front-loaded with the main action. It is informative without being verbose, though could be slightly restructured for easier scanning (e.g., bullet points). Overall, it balances detail and conciseness well.
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 presence of an output schema (not shown but indicated) and three optional parameters, the description covers the essential functionality. It doesn't mention geographic scope or prerequisites, but the tool is straightforward and the sibling tool likely handles broader guidance. The description is complete enough for an agent to understand when to invoke it.
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 descriptions, but the description adds value by noting optionality and the relationship between business_name and website (provide one or the other). It also clarifies that location and website improve accuracy, which is additional context beyond the schema.
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 checks if a local business appears in AI engine recommendations and provides a report with fixes. It uses specific verbs ('check', 'get') and resources ('AI engines', 'Radveo report'), and distinguishes from the sibling tool by focusing on current visibility assessment vs. general optimization guidance.
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 explains the tool's purpose and output, and notes that it improves odds without guaranteeing placement. It implicitly guides usage by mentioning what inputs to provide (website, business_name, location). However, it could more explicitly state when not to use it or how it differs from the sibling, but overall clear enough.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
how_to_appear_in_ai_searchAInspect
Get Radveo's honest, practical playbook for getting a local business found and cited inside AI engine answers (ChatGPT, Gemini, Google AI Overviews) — answer-engine optimization (AEO) steps any owner can act on.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| ok | Yes | |
| note | Yes | Honesty framing: these steps improve odds, they never guarantee placement. |
| steps | Yes | The AEO steps, in order. Empty only when the server is rate-limiting. |
| freeCheckUrl | Yes | Where to run the free AI-visibility check. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It clearly indicates the tool returns a playbook with actionable steps and is read-only. While it could mention cost or that it's static content, the description is sufficient for understanding the tool's behavior.
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 a single, well-structured sentence that front-loads the key action ('Get...playbook') and provides essential context. Every word contributes meaning, with no 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?
Given the tool has no parameters and an output schema exists (providing structure for the playbook), the description is complete. It clearly explains what the tool does and what the user will receive.
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 no parameters, and schema description coverage is 100%. The description adds value by explaining what the tool returns (a practical playbook), which goes beyond just stating no parameters.
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 identifies the tool as providing a specific resource ('Radveo's honest, practical playbook') for a clear purpose ('getting a local business found and cited inside AI engine answers'). It distinguishes itself from the sibling tool 'check_ai_visibility' by offering actionable steps rather than just checking visibility.
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 implies use when seeking a practical playbook for answer-engine optimization (AEO), but does not explicitly state when to use this over 'check_ai_visibility' or other alternatives. It could provide clearer guidance on scenarios where this tool is appropriate.
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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