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Argosor — Unified SEO, SERP, Clean Markdown & GEO (AI Citation Visibility) MCP Server

by com.argosor
OverviewDiscussionsAdmin

AI Bot Log Telemetry, WAF Spoofing & Analytics Attribution

agent_crawler_traffic_attribution
Read-onlyIdempotent

Analyze domain-level server log bot-hit telemetry, edge WAF bot-spoofing behavior, and AI referral attribution across Model Training Crawlers, AI Search Indexers, and User-Triggered Agent Browsers (distinct from 'technical_seo_crawl', which inspects single-page HTML DOM and HTTP headers). Use this tool to detect Cloudflare or AWS WAF rules blocking legitimate AI search bots on 'domain' and to generate referral attribution regex filters for your 'analytics_platform'. Returns 3-tier bot telemetry classifications, edge WAF spoofing diagnostics, and ready-to-paste GA4, Plausible, PostHog, and Nginx regex rules.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "domain": "<domain>"
    },
    "name": "agent_crawler_traffic_attribution"
  }
}

Input Schema

NameRequiredDescriptionDefault
domainYesTarget root domain to audit AI bot log telemetry, WAF spoofing, and referral attribution rules for (e.g., "argosor.com").
analytics_platformNoTarget analytics platform for generated referral attribution regex snippets (default: "all").all

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • changedInput schema / properties / analytics_platform / description
      Previous value: -"Target analytics platform for generated regex attribution snippets (default: \"all\")."New value: +"Target analytics platform for generated referral attribution regex snippets (default: \"all\")."
    • changedInput schema / properties / domain / description
      Previous value: -"Target root domain to audit AI crawler permissions and generate attribution rules for (e.g., \"argosor.com\")."New value: +"Target root domain to audit AI bot log telemetry, WAF spoofing, and referral attribution rules for (e.g., \"argosor.com\")."
  2. Changed5 schema fields changed
    • removedInput schema / properties / analyticsPlatform
      Removed value: -{
-  "description": "Target analytics platform for attribution snippets. Defaults to \"all\".",
-  "enum": [
-    "all",
-    "ga4",
-    "plausible",
-    "posthog"
-  ],
-  "type": "string"
-}
    • addedInput schema / properties / analytics_platform
      Added value: +{
+  "default": "all",
+  "description": "Target analytics platform for generated regex attribution snippets (default: \"all\").",
+  "enum": [
+    "all",
+    "ga4",
+    "plausible",
+    "posthog"
+  ],
+  "type": "string"
+}
    • changedInput schema / properties / domain / description
      Previous value: -"Target root domain to audit AI crawler taxonomy and generate analytics attribution filters for (e.g., \"argosor.com\")."New value: +"Target root domain to audit AI crawler permissions and generate attribution rules for (e.g., \"argosor.com\")."
    • addedInput schema / properties / domain / maxLength
      Added value: +253
    • addedInput schema / properties / domain / minLength
      Added value: +3
  3. First observed

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=true, so the safety profile is covered. The description adds genuinely new behavioral context: the output tiers (3-tier bot telemetry classification), edge WAF spoofing diagnostics, and ready-to-paste regex artifacts per platform. It does not mention auth requirements, rate limits, or latency, which is why it stops short of 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, ordered what-it-does → when-to-use → what-it-returns, which is the right front-loading. The first sentence is dense with jargon and parenthetical asides, but every clause carries routing or output information rather than filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only, open-world, two-parameter analysis tool with no output schema, the description covers purpose, scope, exclusions, and return contents. The remaining gap is operational: nothing about expected runtime, whether the domain must be verified/owned, or how WAF access is obtained for the spoofing diagnostics.

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?

Schema description coverage is 100%, so both parameters are already documented with types, enum values, defaults, and length bounds. The description reinforces the roles of 'domain' and 'analytics_platform' but adds no syntax, format, or edge-case detail beyond the schema; noting that Nginx regex rules are returned even though Nginx is not an 'analytics_platform' enum value is the only extra signal, and it concerns output rather than input.

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 ('Analyze') over three named resources: server log bot-hit telemetry, edge WAF bot-spoofing behavior, and AI referral attribution. It also enumerates the three bot classes it covers and explicitly distinguishes itself from the sibling technical_seo_crawl, so an agent can route without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides an explicit 'use this tool to' trigger (detect Cloudflare/AWS WAF rules blocking legitimate AI search bots on 'domain', generate referral attribution regex filters for 'analytics_platform') and a named exclusion ('distinct from technical_seo_crawl, which inspects single-page HTML DOM and HTTP headers'). Both the positive and negative selection criteria are stated.

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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