Bot traffic detector for paid ads. Input: domain. Returns trigger, fake sessions, daily USD waste.
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Average 4/5 across 1 of 1 tools scored.
Only one tool exists, so there is no possibility of confusing it with another. Its purpose is clearly defined."
The tool name follows a clear verb_noun pattern ('check_ad_traffic'), and with only one tool, consistency is inherently maintained.
A single tool is too few for a server named 'botguard-pro' which implies a broader bot protection service. The lone detection tool feels under-scoped for the apparent purpose.
The server only provides detection of bot traffic and wasted spend. Missing are expected capabilities like blocking traffic, detailed analytics, configuration, or remediation, leaving obvious gaps in the domain.
check_ad_trafficAInspectDetects bot traffic and wasted ad spend for a site. Input: site_id (string). Returns: trigger (bool), paid_sessions (int), estimated_wasted_per_day (float in USD). Use when diagnosing ROAS drops or invalid clicks on paid campaigns.
| Name | Required | Description | Default |
|---|---|---|---|
| site_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must convey behavioral traits. It does state the nature of the tool ('detects') and gives the return format, implying a read-only analysis. However, it does not explicitly confirm the absence of side effects, permissions, or other behavioral constraints, leaving room for assumptions.
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 compact and well-structured: statement of purpose, input, return values, and usage scenario. Every sentence adds value, with no redundancy or filler.
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 tool with one parameter and no output schema or annotations, the description covers all essential aspects: what it does, what input it takes, what output it returns, and when to use it. It is sufficiently complete for an agent to select and invoke the tool correctly.
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 zero description coverage, and the tool description only repeats 'Input: site_id (string)' without adding any meaning. It does not explain what a site ID is, its format, or how to obtain it. This fails to compensate for the missing schema descriptions.
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's function: 'Detects bot traffic and wasted ad spend for a site.' The verb 'detects' and the specific resources (bot traffic, ad spend) make the purpose unambiguous, and it is further refined by the scope (for a site). This is distinct and specific.
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 provides explicit usage context: 'Use when diagnosing ROAS drops or invalid clicks on paid campaigns.' This clearly indicates when to apply the tool. However, it does not mention alternatives or when not to use it, so it falls just short of a 5.
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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{
"$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.
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