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jakedx6

Helios-9 MCP Server

by jakedx6

get_project_insights

Analyze project progress, bottlenecks, team performance, documentation health, and AI readiness with actionable recommendations.

Instructions

Get deep analytics and insights for a specific project

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idYesThe project ID to analyze
insight_typesNoTypes of insights to generate
include_recommendationsNoWhether to include actionable recommendations

Implementation Reference

  • Handler function that parses args, fetches project data via supabaseService, runs each requested insight type (progress, bottlenecks, team_performance, documentation_health, ai_readiness), optionally generates recommendations, and returns an overall health score.
    export const getProjectInsights = requireAuth(async (args: any) => {
      const { project_id, insight_types, include_recommendations } = GetProjectInsightsSchema.parse(args)
      
      logger.info('Getting project insights', { project_id, insight_types })
    
      const project = await supabaseService.getProject(project_id)
      const projectContext = await supabaseService.getProjectContext(project_id)
    
      const insights: any = {
        project_overview: {
          id: project.id,
          name: project.name,
          status: project.status,
          created_at: project.created_at,
          updated_at: project.updated_at
        }
      }
    
      // Generate requested insights
      for (const insightType of insight_types) {
        switch (insightType) {
          case 'progress':
            insights.progress_analysis = analyzeProjectProgress(projectContext)
            break
          case 'bottlenecks':
            insights.bottleneck_analysis = identifyProjectBottlenecks(projectContext)
            break
          case 'team_performance':
            insights.team_performance = analyzeTeamPerformance(projectContext)
            break
          case 'documentation_health':
            insights.documentation_health = analyzeDocumentationHealth(projectContext)
            break
          case 'ai_readiness':
            insights.ai_readiness = assessProjectAIReadiness(projectContext)
            break
        }
      }
    
      if (include_recommendations) {
        insights.recommendations = generateProjectRecommendations(insights, projectContext)
      }
    
      insights.overall_health_score = calculateOverallHealthScore(insights)
    
      return insights
    })
  • Zod schema defining input validation: project_id (uuid, required), insight_types (array of enums, default [progress,bottlenecks]), and include_recommendations (boolean, default true).
    const GetProjectInsightsSchema = z.object({
      project_id: z.string().uuid(),
      insight_types: z.array(z.enum(['progress', 'bottlenecks', 'team_performance', 'documentation_health', 'ai_readiness'])).default(['progress', 'bottlenecks']),
      include_recommendations: z.boolean().default(true)
    })
  • MCPTool registration object with name 'get_project_insights', description, and inputSchema for MCP tool discovery.
    export const getProjectInsightsTool: MCPTool = {
      name: 'get_project_insights',
      description: 'Get deep analytics and insights for a specific project',
      inputSchema: {
        type: 'object',
        properties: {
          project_id: {
            type: 'string',
            format: 'uuid',
            description: 'The project ID to analyze'
          },
          insight_types: {
            type: 'array',
            items: {
              type: 'string',
              enum: ['progress', 'bottlenecks', 'team_performance', 'documentation_health', 'ai_readiness']
            },
            default: ['progress', 'bottlenecks'],
            description: 'Types of insights to generate'
          },
          include_recommendations: {
            type: 'boolean',
            default: true,
            description: 'Whether to include actionable recommendations'
          }
        },
        required: ['project_id']
      }
    }
  • Exports the handler under the key 'get_project_insights' in contextAggregationHandlers, which is merged into the main allHandlers in src/index.ts.
    export const contextAggregationHandlers = {
      get_smart_context: getSmartContext,
      get_workspace_overview: getWorkspaceOverview,
      get_project_insights: getProjectInsights,
      find_related_content: findRelatedContent,
      generate_context_summary: generateContextSummary
    }
  • Resource routing: when a resource URI matches '/project/{id}/health', it calls get_project_insights with all insight types enabled.
    const projectHealthMatch = path.match(/^\/project\/([^\/]+)\/health$/)
    if (projectHealthMatch) {
      return await this.allHandlers.get_project_insights({ 
        project_id: projectHealthMatch[1],
        insight_types: ['progress', 'bottlenecks', 'team_performance', 'documentation_health'],
        include_recommendations: true
      })
    }
Behavior2/5

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

No annotations are provided. The description does not disclose any behavioral traits such as computational cost, permission requirements, or side effects. The phrase 'deep analytics' hints at complexity but lacks concrete details.

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?

A single sentence effectively communicates the tool's purpose. It is concise and front-loaded, though it could be slightly expanded without compromising conciseness.

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

Completeness2/5

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

For a tool generating insights, the description does not mention the output format, structure, or any return values. No output schema is provided. The description is too brief to be fully complete given the tool's complexity.

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?

Input schema has 100% description coverage for all three parameters. The schema already explains each parameter clearly. The description adds no extra meaning beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get' and the resource 'deep analytics and insights for a specific project'. It is specific enough to convey the tool's function, though it does not explicitly differentiate from siblings like get_project_analytics or get_initiative_insights.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives (e.g., get_project_analytics, get_initiative_insights). There are no indications of prerequisites, appropriate contexts, or exclusions.

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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