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JackJProsp

Prosp MCP Server

by JackJProsp

Import Leads

import_leads

Add structured leads to a Prosp list, requiring a LinkedIn URL and preserving extra fields as custom variables for personalized outreach.

Instructions

Import leads from structured data.

Each lead needs a linkedin_url. Any other key becomes a custom variable usable in message prompts, so a signal column recorded during research travels with the lead into the copy.

Example lead: {"linkedin_url": "...", "first_name": "...", "signal": "hiring a RevOps lead, posted 14 Mar", "signal_url": "...", "tier": "1"}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
leadsYes
list_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
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 does disclose meaningful behavior — that each lead requires linkedin_url and that arbitrary extra keys become custom variables usable in message prompts — which is genuinely useful. It omits permissions, duplicate handling, list append-vs-overwrite semantics, and partial-failure behavior.

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?

Front-loaded with the core action, then the two most important behavioral facts, then a compact concrete example. The example earns its length because the schema is opaque; nothing reads as filler.

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?

An output schema exists, so return values need no explanation. Still, for a required-parameter mutation tool with no annotations and an undescribed list_id, an agent lacks enough to call it confidently against its import siblings.

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 coverage is 0% and the leads array is an opaque additionalProperties:true object, so the description's explanation of the lead shape (linkedin_url mandatory, everything else becomes a custom variable) is essential and it delivers that. But list_id is never explained anywhere, and there is no guidance on multi-lead array limits or key naming rules.

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 specific verb+resource ('Import leads') and adds the crucial qualifier 'from structured data', which hints at the distinction from import_from_post/import_from_search. However, it never explicitly contrasts itself with those siblings, so the boundary must be inferred.

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 phrase 'from structured data' plus the worked example implies usage, but there is no explicit when-to-use statement and no pointer to import_from_post or import_from_search for the other ingestion paths — a real gap given three import siblings exist.

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