Skip to main content
Glama
jakedx6

Helios-9 MCP Server

by jakedx6

get_team_productivity

Analyze team productivity patterns and performance across multiple dimensions including task completion, collaboration, and code quality within specific timeframes and project contexts.

Instructions

Analyze team productivity patterns and performance

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
team_membersNoSpecific team member IDs to analyze (optional)
project_idNoProject context for analysis (optional)
time_rangeNoTime range for analysismonth
productivity_dimensionsNoDimensions of productivity to measure

Implementation Reference

  • The main handler function for the get_team_productivity tool. It parses input using the schema, fetches team data, analyzes productivity dimensions, calculates overall score, generates insights, and returns the productivity analysis.
    export const getTeamProductivity = requireAuth(async (args: any) => {
      const { team_members, project_id, time_range, productivity_dimensions } = GetTeamProductivitySchema.parse(args)
      
      logger.info('Analyzing team productivity', { team_members, project_id, time_range })
    
      // Get team data
      const teamData = await getTeamData(team_members, project_id, time_range)
      
      const productivity: any = {
        time_range,
        team_size: teamData.members.length,
        project_context: project_id,
        analyzed_at: new Date().toISOString(),
        dimensions: {}
      }
    
      // Analyze each productivity dimension
      for (const dimension of productivity_dimensions) {
        try {
          switch (dimension) {
            case 'task_completion':
              productivity.dimensions.task_completion = analyzeTaskCompletion(teamData)
              break
            case 'collaboration':
              productivity.dimensions.collaboration = analyzeCollaboration(teamData)
              break
            case 'code_quality':
              productivity.dimensions.code_quality = analyzeCodeQuality(teamData)
              break
            case 'documentation':
              productivity.dimensions.documentation = analyzeDocumentation(teamData)
              break
            case 'mentoring':
              productivity.dimensions.mentoring = analyzeMentoring(teamData)
              break
            case 'innovation':
              productivity.dimensions.innovation = analyzeInnovation(teamData)
              break
          }
        } catch (error) {
          logger.error(`Failed to analyze ${dimension}:`, error)
          productivity.dimensions[dimension] = { error: 'Analysis failed' }
        }
      }
    
      // Calculate overall productivity score
      productivity.overall_score = calculateOverallProductivityScore(productivity.dimensions)
      
      // Generate team insights
      productivity.insights = generateTeamInsights(productivity.dimensions, teamData)
      productivity.improvement_suggestions = generateImprovementSuggestions(productivity.dimensions)
    
      return productivity
    })
  • Zod schema for validating input parameters to the get_team_productivity tool, including team_members, project_id, time_range, and productivity_dimensions.
    const GetTeamProductivitySchema = z.object({
      team_members: z.array(z.string()).optional(),
      project_id: z.string().optional(),
      time_range: z.enum(['week', 'month', 'quarter']).default('month'),
      productivity_dimensions: z.array(z.enum(['task_completion', 'collaboration', 'code_quality', 'documentation', 'mentoring', 'innovation'])).default(['task_completion', 'collaboration'])
    })
  • MCPTool registration defining the get_team_productivity tool with name, description, and input schema (JSON schema version).
    export const getTeamProductivityTool: MCPTool = {
      name: 'get_team_productivity',
      description: 'Analyze team productivity patterns and performance',
      inputSchema: {
        type: 'object',
        properties: {
          team_members: {
            type: 'array',
            items: { type: 'string' },
            description: 'Specific team member IDs to analyze (optional)'
          },
          project_id: {
            type: 'string',
            description: 'Project context for analysis (optional)'
          },
          time_range: {
            type: 'string',
            enum: ['week', 'month', 'quarter'],
            default: 'month',
            description: 'Time range for analysis'
          },
          productivity_dimensions: {
            type: 'array',
            items: {
              type: 'string',
              enum: ['task_completion', 'collaboration', 'code_quality', 'documentation', 'mentoring', 'innovation']
            },
            default: ['task_completion', 'collaboration'],
            description: 'Dimensions of productivity to measure'
          }
        }
      }
    }
  • Export mapping handlers to tool names, registering get_team_productivity to getTeamProductivity function.
    export const analyticsInsightsHandlers = {
      get_project_analytics: getProjectAnalytics,
      get_team_productivity: getTeamProductivity,
      get_workspace_health: getWorkspaceHealth,
      generate_custom_report: generateCustomReport
    }
  • Export grouping all analytics tools, including getTeamProductivityTool.
    export const analyticsInsightsTools = {
      getProjectAnalyticsTool,
      getTeamProductivityTool,
      getWorkspaceHealthTool,
      generateCustomReportTool
    }
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure but offers minimal information. It doesn't describe what the analysis entails, how results are returned, whether it's a read-only operation, potential rate limits, authentication requirements, or data freshness. The vague phrase 'analyze patterns and performance' leaves critical behavioral aspects unspecified.

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?

The description is a single, efficient sentence that states the tool's purpose without unnecessary words. It's appropriately sized for a tool with good schema documentation, though it could be more front-loaded with critical information given the lack of annotations and output schema.

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?

Given the complexity of a productivity analysis tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what kind of analysis is performed, what metrics are calculated, how results are structured, or what insights can be expected. The agent lacks sufficient context to understand what this tool actually delivers.

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?

The input schema has 100% description coverage, providing clear documentation for all 4 parameters. The description adds no additional parameter semantics beyond what's already in the schema. According to scoring rules, when schema_description_coverage is high (>80%), the baseline score is 3 even with no parameter information in the description.

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

Purpose3/5

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

The description states the tool's purpose as analyzing team productivity patterns and performance, which is clear but vague. It specifies the general domain (team productivity) but lacks concrete details about what specific analysis it performs or what outputs it generates. It doesn't differentiate from sibling tools like 'get_project_analytics' or 'get_workspace_health' that might also provide productivity-related 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?

The description provides no guidance on when to use this tool versus alternatives. There are no explicit statements about when this tool is appropriate, when to avoid it, or what other tools might serve similar purposes. The agent must infer usage from the tool name and parameters alone, which is insufficient for optimal selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/jakedx6/helios9-MCP-Server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server