Fortune 500 AI Business Operating System - a governance-aware MCP server harness for multi-agent CEO/executive workflows with decision authority matrix enforcement
This is a configuration wrapper around @apify/actors-mcp-server, not a standalone MCP server implementation. The .mcp.json file shows tool invocation via npx but provides NO visible tool definitions, schemas, parameter descriptions, or error handling. All 11 tools are declared by name only in the --tools argument. No input schemas, output schemas, parameter types, or descriptions are visible in the provided source. The governance hooks are shell scripts that post-process Claude tool calls, they do not define MCP tools. Without access to the actual @apify/actors-mcp-server source code, tool quality cannot be assessed. The average of 11 tools capped at 50 is 50, but given the complete absence of visible definitions, descriptions, and schemas in THIS repository, the score reflects what IS present: configuration only.
Apify Actors tool - execute and manage Apify actors
Apify Cheerio scraper - lightweight web scraping using Cheerio
Apify Google Search scraper - scrape Google search results
Apify RAG web browser actor - web scraping and content extraction with RAG capabilities
Apify website content crawler - crawl and extract website content
Apify documentation tool - access Apify documentation and API reference
Advanced news scraper - scrape and aggregate news content
No input schemas visible for any tool.
Tool definitions are inferred from --tools CLI argument only. No explicit MCP tool registration visible in source.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 23 | 2026-07-28+ | v2 |
LinkedIn scraper actor - extract leads and profile data from LinkedIn
Apify runs tool - manage and monitor actor runs
SEC EDGAR filing scraper - extract SEC company filing data
Apify storage tool - access and manage Apify storage (datasets, key-value stores)
Descriptions are generic one-liners (20 chars). Baseline for A+ tools is 50-200 chars with WHAT, WHEN, and prerequisites. Current descriptions do not meet minimum actionability threshold.
No parameter descriptions visible. Rubric requires every parameter to have a description explaining what it controls. LLMs cannot infer parameter meaning from names alone.
No error handling guidance visible. Tools must return actionable error messages that tell LLMs what to do next (retry, ask user, or fail). Current implementation provides no recovery guidance.