A
licenseNot graded
qualityC
maintenanceEnables measuring whether AI assistants actually recommend a brand when buyers ask for a recommendation, returning visibility check results as three-band verdicts and counts.
21 npm
1
MIT
w0-mcp-server
Server Details
Look up a brand's AI Recommendation Rate: how often it's the #1 AI pick.
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- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP · MCP 2025-06-18
- URL
- Repository
- modern-ai-inc/w0-mcp-server
- GitHub Stars
- 0
- Server Listing
- w0-mcp-server
TDQS
A4.2/5.0
Scored across 1 tool
Disambiguation5/5
There is only one tool, so no ambiguity or overlap exists. Its purpose is clearly described as looking up a brand's AI Recommendation Rate and Inclusion Rate. Agents cannot confuse it with any other tool.
Naming Consistency4/5
The single tool name follows a clean snake_case verb_noun pattern and is descriptive. However, with only one tool, there is insufficient evidence to confirm a consistent naming convention across a tool set, so a perfect score is not given.
Tool Count3/5
At one tool, the server is on the thin end of the scale. For a narrowly scoped free lookup endpoint it is acceptable, but it feels minimal compared to typical multi-tool MCP servers. A couple of related lookup or comparison tools would make the count more robust.
Completeness4/5
The tool fully delivers its stated purpose by returning both the AI Recommendation Rate and Inclusion Rate for a single brand. It lacks multi-brand or historical lookup options, but those are outside the explicit 'single-brand lookup only' scope, so the gap is minor.
Available Tools
1 toollookup_brand_recommendation_rateAInspect
Look up a brand's AI Recommendation Rate: the percentage of buyer-intent questions where the brand is the #1 AI pick (not just mentioned), plus its Recommendation Inclusion Rate (appears anywhere in the answer). Free, single-brand lookup only -- rate-limited per Modern AI's published anti-scrape policy.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| brand | Yes | Brand name to look up, e.g. "Brumate" |
TDQS
A4.3/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses that the operation is a rate-limited, single-brand lookup, which is meaningful behavioral context beyond the schema. It does not cover auth or error behavior, but the essential characteristics are clearly stated.
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 front-load the purpose and distinguish the two metrics. Every clause adds value, including the single-brand and rate-limit caveats, with no wasted words.
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 one-parameter lookup with no output schema, the description specifies exactly what is returned (the two rates) and the constraints on invocation. Nothing an agent needs to decide whether to call it or interpret the result is missing.
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 the brand parameter at 100% coverage, including an example. The description adds no parameter-specific detail beyond the schema, so the baseline 3 applies.
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?
States a specific verb ('Look up') and resource (brand's AI Recommendation Rate), with precise definitions of the two metrics returned. The description makes the tool's function unambiguous and distinguishes it from mere mention counting.
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?
Provides explicit constraints: free, single-brand lookup only, and rate-limited per anti-scrape policy. It does not name alternatives, but with no siblings this is sufficient context for when the tool should be used.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
- First observed
lookup_brand_recommendation_rate
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