get_sales_analyticsSales pipeline analytics for the caller: probability-weighted forecast, per-stage $ rollup, win rate (won / decided, by count and by value), average sales-cycle days over won deals, and open-deal aging with a stale count. Read-only; computed from the caller's own deals. Requires authentication.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Output schema / (root)Previous value: -{
- "additionalProperties": false,
- "properties": {
- "text": {
- "type": "string"
- }
- },
- "required": [
- "text"
- ],
- "type": "object"
-}New value: +nullDoes the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only, idempotent, and non-destructive behavior. The description adds value by stating 'Requires authentication' and clarifying the data scope ('caller's own deals'), plus enumerating the computed outputs. This goes beyond the annotations without contradicting them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no filler. It front-loads the purpose ('Sales pipeline analytics for the caller'), lists metrics in a scannable comma-separated format, and appends read-only/auth notes at the end. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description takes on the burden of explaining what will be returned; it does so by listing the key metrics. It also covers auth and data scope. It could be slightly more explicit about the response structure or whether this is a point-in-time snapshot, but it is largely complete for a zero-parameter read-only tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the schema is empty, so parameter semantics are not applicable. The baseline for 0 parameters is 4; the description appropriately focuses on the data scope and outputs rather than parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific resource ('sales pipeline analytics') and enumerates the exact metrics computed (forecast, per-stage rollup, win rate, sales-cycle days, aging). It also scopes the tool to 'the caller' and 'the caller's own deals,' which distinguishes it from team-level or campaign-level siblings like get_sales_recommendations or campaign_pipeline_stats.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context ('for the caller', 'computed from the caller's own deals') but does not explicitly state when to choose this tool over alternatives or provide exclusions. It gives clear scope but no direct when-not-to-use guidance relative to sibling analytics tools.
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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