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SetLuca

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by setluca

List leads

luca_leads_list
Read-onlyIdempotent

Retrieve leads from a Luca workspace, filter by channel, search by query, and paginate with cursor/limit to review or export prospect lists.

Instructions

List leads. Query supports limit, cursor, channel, and q according to Luca OpenAPI.

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo
maxPagesNoMax pages the server auto-fetches and merges (default 10, hard cap 50). If more remain, the result is marked truncated with a nextCursor to continue.
workspaceIdNoOptional Luca workspace id override.
workspaceSlugNoOptional Luca workspace slug override.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
provenanceNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already cover read-only, idempotent, non-destructive behavior, so the description's added value is the auth/sensitivity context: required scopes (leads:read, luca:read) and data sensitivity: full_content. This is genuinely useful disclosure beyond the annotations, though 'does not require idempotency' just restates idempotentHint=true.

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

Conciseness3/5

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

The core purpose is front-loaded, but the body is a metadata dump ('according to Luca OpenAPI', operation line, scope lines, idempotency line) with some redundancy against the annotations. It is not bloated, but several lines do not earn their place.

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

Completeness4/5

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

An output schema exists, so return values need not be explained. The description covers scopes, sensitivity, and workspace override, which is the context an agent needs; only the pagination/truncation role of maxPages is left solely to the schema.

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?

Schema description coverage is 75%, so both schema and description carry some burden. The description names the query filters (limit, cursor, channel, q) and the workspace overrides, but adds no meaning beyond the parameter names, and omits maxPages entirely (its semantics live only in the schema).

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?

States a clear verb+resource ('List leads') and even names the underlying operation (GET /api/leads). It does not distinguish itself from close siblings such as luca_find_leads or luca_leads_get, so an agent must infer the boundary from names alone.

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?

It gives one concrete usage instruction — use workspaceId/workspaceSlug to override the environment-configured default workspace. But it never says when to choose this list operation over sibling alternatives like luca_find_leads, leaving selection largely to inference.

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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