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by setluca
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TypeScript
Remote

Read the revenue forecast

luca_analytics_forecast
Read-onlyIdempotent

Forecast pipeline revenue with on-track, optimistic, and conservative scenarios based on stage probabilities and historical conversion rates.

Instructions

Point-in-time pipeline revenue forecast with on-track, optimistic, and conservative scenarios based on pipeline stage probabilities and historical conversion rates.

Luca operation: GET /api/analytics/forecast Required scopes: analytics:read Required OAuth scope: luca:read; data sensitivity: redacted Does not require idempotency. Use workspaceId or workspaceSlug to override the default workspace configured by environment.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "luca_analytics_forecast"
  }
}

Input Schema

NameRequiredDescriptionDefault
workspaceIdNoOptional Luca workspace id override.
workspaceSlugNoOptional Luca workspace slug override.

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, non-destructive behavior. The description adds useful operational detail beyond them: required analytics and OAuth scopes, redacted data sensitivity, and the fact that no idempotency key is required. It does not mention rate limits or freshness, so it is not a 5.

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?

The description is front-loaded with the core forecast concept, followed by concise operational metadata. Every line is useful and there is no redundant padding.

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

Completeness5/5

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

With an output schema present and rich annotations, the description sufficiently covers what the tool returns, required scopes, sensitivity, and workspace override behavior. An agent has enough information to invoke it correctly.

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%, so the schema already documents both optional workspace parameters. The description adds meaning by explaining that workspaceId or workspaceSlug override the environment default workspace, which goes beyond the per-parameter schema text.

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 a specific resource and output: a point-in-time pipeline revenue forecast with on-track, optimistic, and conservative scenarios. It does not explicitly differentiate this tool from sibling analytics tools like luca_analytics_revenue, so it falls short of the sibling-differentiation bar for a 5.

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?

The description provides invocation prerequisites such as required scopes and how to override the default workspace, which implies usage context. However, it does not explicitly state when to choose this forecast tool versus alternatives such as luca_analytics_revenue.

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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