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scout.subscribe_lead_feed

Subscribe to a continuous feed of new seller-signal leads matching a filter. As leads are created and approved, Scout POSTs each one to your webhook URL up to your monthly_lead_budget. Ideal for AI agents that act on leads automatically. Currently in beta — emails Spencer with your subscription request; webhook delivery starts after manual provisioning.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filterNo
webhook_urlYesHTTPS endpoint that will receive POST {lead, signature, timestamp}.
monthly_lead_budgetYesCap on number of leads pushed per month.

TDQS

A4.2/5.0
Behavior4/5

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 transparently discloses the beta state, manual onboarding ('emails Spencer'), and that leads are created and approved before posting. It could be improved by mentioning error handling or cancellation process.

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 concise, with three sentences that front-load the purpose and then provide behavioral context and caveats. No redundant or unnecessary words.

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?

Given no output schema and no annotations, the description adequately sets expectations: continuous feed, webhook delivery, budget cap, beta state, manual setup. Missing details on how to cancel or what the POST payload includes (partially in schema but not in description) prevent a perfect score.

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?

The schema covers 67% of parameters with descriptions (webhook_url and monthly_lead_budget). The description adds context by explaining that monthly_lead_budget caps leads pushed. However, the filter parameter lacks details on its sub-properties (state, min_score, signal_types) beyond being mentioned as 'matching a filter.'

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?

The description clearly states the action ('subscribe') and the resource ('continuous feed of new seller-signal leads matching a filter'). It distinguishes from sibling tools like search_leads (one-time query) and purchase_lead (single lead) by emphasizing continuous delivery via webhook.

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

Usage Guidelines4/5

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

The description explicitly says 'Ideal for AI agents that act on leads automatically,' providing clear context for use. It also notes the beta status and manual provisioning, which sets expectations. However, it does not explicitly state when not to use or mention alternatives.

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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TDQS

A4.2/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose, from agent profiles and comparisons to market data and lead management. Overlaps like find_agent vs find_public_agent are explicitly differentiated by live vs directory status.

Naming Consistency4/5

All tools use snake_case and the 'scout.' prefix, but they mix verb_noun (e.g., compare_agents, search_listings) and noun_noun (e.g., agent_profile, coverage) patterns. However, the naming remains predictable and readable overall.

Tool Count5/5

With 18 tools, the server covers agent discovery, brokerage info, market data, listings, lead management, referrals, and licensing—well-scoped for a real estate assistant without redundancy.

Completeness5/5

The tool set covers all key workflows: agent and brokerage search, market insights, school data, listing search, lead generation and purchase, referral management, and license verification, with no obvious gaps for its domain.