ai-economy-infrastructure
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
| resources | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| ai_economy_routerA | Intelligent routing to the right specialist MCP server(s) based on natural language query. Routes to best MCP, returns aggregated results from multiple servers if needed. |
| ai_governance_assessB | Unified AI governance assessment combining CSOAI governance and CASA certification frameworks. Provides risk classification, applicable crosswalks, CASA tier recommendation, compliance gaps, certification pathway, and cost/timeline estimates. |
| ai_sector_complianceB | Sector-branded compliance packages pulling from relevant crosswalks, standards, and certifications. Provides tailored compliance bundles for specific sectors (finance, healthcare, defence, etc.) |
| ai_economy_dashboardA | Cross-ecosystem analytics and intelligence dashboard. Returns usage metrics across all 10 MCPs, compliance posture score, learning progress, security status, PQC readiness, and recommended actions. |
| ai_trust_scoreA | Unified AI trust scoring combining all ecosystem signals. Composite score (0-100) from governance compliance, security posture, PQC readiness, content verification status, and training completion. |
| ai_learning_pathwayB | Cross-ecosystem learning recommendations feeding into OneOS MOOC. Provides personalized learning pathway across BMCC Cyber, CSGA training, OneOS courses, K.A.T.A. belts, and CASA certification prep. |
| ai_data_pipelineB | Data collection configuration for MOOC/analytics integration. Configures data pipelines across MCPs, provides aggregated insights, and enables OneOS integration. |
| ai_market_intelligenceA | Cross-ecosystem market intelligence and opportunity assessment. Analyzes market size, regulatory landscape, competitor activity, and recommends CSOAI services. |
| ai_incident_commandA | Cross-ecosystem incident response coordination. Coordinates response across CSGA, CSOAI, QuantraNet, and PROOFOF. Handles cyber, AI safety, compliance, and quantum threats. |
| ai_certification_bundleC | Multi-certification pathway across ecosystem. Bundles CASA + CSR5 + K.A.T.A. + PQC assessment with unified timeline, single point of contact, and combined pricing. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| AI Economy Ecosystem Map | Complete map of all 10 specialist MCP servers, their capabilities, and cross-ecosystem integration points |
| Supported Sectors and Compliance Packages | All supported sectors and sector-specific compliance package definitions |
| AI Trust Scoring Framework | Detailed trust scoring methodology, dimensions, and score interpretations |
| Data Collection Schema | Data schema for OneOS MOOC integration and ecosystem analytics |
TDQS
Scored across 10 tools
Each tool targets a distinct functional area such as routing, governance, compliance, analytics, learning, data, market intelligence, incident response, certification, and trust scoring. However, some overlap exists between ai_governance_assess and ai_sector_compliance, and between ai_certification_bundle and ai_learning_pathway, though descriptions help clarify their differences.
All tools use a snake_case naming convention with an 'ai_' prefix, which is predictable and consistent. The pattern is mostly noun-based descriptors, with 'ai_governance_assess' being the only tool that mixes in a verb, creating a minor deviation.
The 10 tools are well-scoped for a server that provides a comprehensive overview of an AI economy ecosystem, covering multiple strategic aspects. This fits comfortably within the ideal 3-15 range, with each tool earning its place.
The tool set covers the major facets of an AI economy infrastructure, including routing, governance, compliance, analytics, learning, data integration, market intelligence, incident response, certification, and trust scoring. Minor gaps exist, such as a dedicated user management or policy tool, but these are not core to the server's evident purpose.