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Modus MCP Server

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by mod-us

modus_get_sales_insights

Analyze sales performance across 30 categories including revenue gaps, attrition risk, and pipeline coverage with AI-powered insights and confidence scores.

Instructions

Get AI-powered sales insights across 30+ categories including revenue gaps, attrition risk, territory performance, pipeline coverage, and competitive analysis. Returns detailed insights with recommendations and confidence scores.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoriesNoComma-separated list of categories (e.g., 'REVENUE_GAP,ATTRITION_RISK,TERRITORY_PERFORMANCE'). Available: REVENUE_GAP, HEADCOUNT_PLANNING, CAPACITY_UTILIZATION, ATTRITION_RISK, ATTRITION_BACKFILLS, PIPELINE_COVERAGE, WIN_RATE_SHIFTS, SALES_CYCLE_BOTTLENECK, TERRITORY_PERFORMANCE, TERRITORY_DESIGN, TERRITORY_LOAD_MGMT, MARKET_EXPANSION, COMPETITIVE_ANALYSIS, SKILLS_GAP, and 20+ more.
timeframeNoJSON timeframe (e.g., '{"months": 12}')
includeRecommendationsNoInclude AI recommendations in results
limitNoMaximum number of insights to return (max: 100)
skipCacheNoForce fresh generation (slower but current). Default: false (uses cached data)

Implementation Reference

  • Handler case for modus_get_sales_insights tool. Parses arguments (categories, timeframe, includeRecommendations, limit, skipCache), calls the Modus API at /api/sales-insights with query parameters, then processes and returns the insights with summary statistics (total count, severity breakdown, category grouping, average confidence, top recommendations).
    case "modus_get_sales_insights": {
      const { categories, timeframe, includeRecommendations = true, limit = 50, skipCache = false } = args || {};
      const params = new URLSearchParams();
    
      if (categories) params.append("categories", categories);
      if (timeframe) params.append("timeframe", timeframe);
      if (includeRecommendations !== undefined) params.append("includeRecommendations", includeRecommendations.toString());
      if (limit) params.append("limit", limit.toString());
      if (skipCache) params.append("skipCache", skipCache.toString());
    
      response = await modusApi.get(`/api/sales-insights?${params.toString()}`);
      const insights = response.data?.insights || [];
    
      // Add summary statistics
      const summary = {
        totalInsights: insights.length,
        bySeverity: countBySeverity(insights),
        byCategory: groupByCategory(insights),
        averageConfidence: insights.reduce((sum, i) => sum + (i.confidence || 0), 0) / insights.length || 0,
        topRecommendations: getTopRecommendations(insights, 3),
      };
    
      return {
        content: [
          {
            type: "text",
            text: JSON.stringify(
              {
                summary,
                insights,
                note: insights.length >= limit ? `Showing first ${limit} insights` : null,
              },
              null,
              2
            ),
          },
        ],
      };
    }
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It describes a read operation returning insights with recommendations and confidence scores, but does not disclose caching behavior, data freshness implications, or any side effects. The skipCache parameter is mentioned in schema but not in description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences. Front-loaded with core purpose and scope. No wasted words.

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

Completeness3/5

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

No output schema exists, so description must cover return structure. It mentions 'detailed insights with recommendations and confidence scores' but lacks specifics on the insight format, field names, or pagination. With 5 parameters including optional categories and limit, more detail on output would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with detailed parameter descriptions. The description adds value by summarizing the available categories (30+) and providing examples, which helps the agent understand the breadth beyond the exhaustive schema list.

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

Purpose5/5

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

Description states specific verb ('Get') and resource ('AI-powered sales insights') across 30+ categories, listing examples. Clearly distinguishes from sibling tools focused on attrition, bench, headcount, etc.

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

Usage Guidelines3/5

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

Description implies use for sales insights across listed categories, but does not explicitly state when to use this tool versus siblings (e.g., modus_get_attrition_risks for concentrated attrition queries). No when-not or alternative guidance is provided.

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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