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Multi-stage MCP server that provides role-based meta-tools (Observability, Security, Automation, Configuration, Compliance, Identity) with dynamic discovery via AGNTCY Directory, fan-out to downstream platform servers, and fusion via INFER intelligence engine
MIGA exhibits critical definition quality gaps across all 13 tools. While tool names follow verb_noun convention (observability, security, automation, etc.), they are semantically vague and do not clearly convey actionable intent. Critical issues: (1) ALL 13 tools have severely under-specified parameter schemas, most declare only a bare 'params' object with minimal type information, violating the requirement that every parameter needs a description and type. (2) Descriptions exist but are generic and formulaic, following a repetitive 'Role-based meta-tool that fans out to registered servers serving the X role' pattern without explaining WHEN to use each tool, WHAT it returns, or HOW to interpret results. (3) NO input schemas are visible in the source code for most tools, only the tool name and bare {'params': {'type': 'object', 'description': '...'}} appear, with no structured parameter definitions. (4) NO output schemas are documented anywhere. (5) Error handling is not visible in the source; no recovery guidance, retryability classification, or actionable error messages are evident. Gateway meta-tools (observability, security, automation, configuration, compliance, identity, network_status, gateway_health) lack specificity about what 'aggregates results' means or how conflicts between servers are resolved. INFER tools (correlate_events, predict_failures, detect_anomalies) have slightly better descriptions but still lack parameter constraints, output structures, and error guidance. Per the HARD SCORING RULES: tools with no visible input schema beyond a generic 'params' object receive schema score 0. Descriptions under 20 characters or generic templates under 50 characters receive description scores capped at 40.
Role-based meta-tool that fans out to registered servers serving the Automation role, aggregates results from platform servers and INFER fusion engine, and returns unified automation insights
Role-based meta-tool that fans out to registered servers serving the Compliance role, aggregates results from platform servers and INFER fusion engine, and returns unified compliance insights
Role-based meta-tool that fans out to registered servers serving the Configuration role, aggregates results from platform servers and INFER fusion engine, and returns unified configuration insights
Returns gateway health status including uptime, registered servers, and routing table metadata
Role-based meta-tool that fans out to registered servers serving the Identity role, aggregates results from platform servers and INFER fusion engine, and returns unified identity insights
Correlates events from multiple platforms using entity overlap and time proximity within a configurable window
No visible input schemas for 8 gateway tools (observability, security, automation, configuration, compliance, identity, network_status, gateway_health). Tool definitions show only generic {'params': {'type': 'object'}} with no enumeration of actual parameters, types, or constraints. Violates JSON Schema best practices and makes tool invocation ambiguous for LLMs.
All 13 tools lack documented output schemas. No response structure, field types, or chaining IDs are documented. LLMs cannot plan downstream tool calls or know what fields to extract from results. Violates pattern:response-shaper and pattern:tool-chain requirements.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 45 | <=2025-11-25 | v2 |
Detects statistical anomalies and unusual patterns across multi-platform telemetry using isolation forest and threshold-based detection
Return service health status for INFER intelligence engine
Predicts potential cascading failures across platforms based on historical patterns and current anomaly detection
Analyzes correlated multi-platform events to identify root cause using expert-curated templates and cross-platform signal pattern matching
Returns aggregated network status from all registered downstream servers via role sweep
Role-based meta-tool that fans out to registered servers serving the Observability role, aggregates results from platform servers and INFER fusion engine, and returns unified observability insights
Role-based meta-tool that fans out to registered servers serving the Security role, aggregates results from platform servers and INFER fusion engine, and returns unified security insights
Descriptions for 8 gateway role-based meta-tools are formulaic and generic ('Role-based meta-tool that fans out to registered servers serving the X role, aggregates results from platform servers and INFER fusion engine, and returns unified X insights'). Do not answer WHEN to use, WHAT the tool does operationally, or HOW to interpret conflicts between multiple server responses. Violates pattern:tool-description requirement for LLM-actionable, context-rich descriptions.
INFER tools (infer_correlate_events, infer_predict_failures, infer_detect_anomalies) declare array parameters (events, platforms) with only bare type 'array' and no itemSchema defining the structure of array elements. LLMs cannot construct valid payloads without seeing CorrelatedEvent structure.
No error handling documentation. No indication whether tools are retryable, whether they classify errors as user-fixable vs fatal, or what recovery actions LLMs should take. Violates pattern:recovery-guide and pattern:error-classification.
Tool names 'observability', 'security', 'automation', 'configuration', 'compliance', 'identity' are nouns, not verb_noun patterns. LLMs expect verb_noun (e.g., 'get_observability_insights', 'check_security_status'). Current naming is ambiguous about the action being invoked.
infer_detect_anomalies declares 'sensitivity' parameter as type 'number' with description '0-1, default 0.85' but no minimum/maximum constraints in schema. Per pattern:constrained-input, numeric params should declare min/max via JSON Schema to prevent LLMs passing invalid values like 10 or -1.
Destructive/write tools (automation, configuration) lack explicit confirmation or dry-run patterns. Per pattern:confirmation-request, irreversible operations should support validation before execution to prevent agent mistakes.
No indication of pagination support for tools that may return large result sets (e.g., infer_correlate_events with many events). Per pattern:paginated-result, tools returning lists should accept limit and offset/cursor parameters.