HeyLead

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signals

Destructive

Identify and analyze LinkedIn buying intent from keywords and job changes to prioritize prospects. Generate reports, track website visitors, and fine-tune classification with feedback.

Instructions

View and analyze buying signals from LinkedIn.

Args:
    action: What to do:
        "show"     — Display detected buying signals (keyword mentions, job changes, etc.)
        "report"   — Signal analytics report with trends and ROI
        "strategy" — Show strategy engine insights, patterns, and autonomous actions
        "feedback" — Mark a signal as 'useful' or 'not_useful' (improves future classification)
        "website_setup" — Set up website visitor tracking (generates JS snippet to embed)
        "website_stats" — View website tracking analytics (visits, companies, high-intent)
        "optimize" — Run signal self-optimization (weights, keywords, warmup, thresholds)
        "optimize_history" — View optimization change log with rollback IDs
        "optimize_rollback" — Rollback a specific optimization change by entry ID
        "optimize_weights" — Show all signal weights (default vs effective overrides)
    campaign_id: Filter by campaign. Shows all if empty.
    signal_type: Filter by signal type, e.g. 'keyword_mention', 'job_change' (for 'show').
        For 'optimize_history': filter by optimization type (weight, keyword_added, warmup, threshold).
    status: Filter by status: 'new', 'classified', 'actioned' (for 'show').
    limit: Max signals to show (for 'show'). Default 20.
    days: Lookback window in days (for 'report'). Default 30.
    signal_id: Signal ID (for 'feedback' action). Entry ID (for 'optimize_rollback').
    feedback: 'useful' or 'not_useful' (for 'feedback' action).

Input Schema

TableJSON Schema

Name	Required	Default
`days`	No
`limit`	No
`action`	No	show
`status`	No
`feedback`	No
`signal_id`	No
`campaign_id`	No
`signal_type`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.10.375Sep 18, 2026

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already signal readOnlyHint=false and destructiveHint=true; the description adds meaningful behavior context beyond those flags: 'website_setup — generates JS snippet to embed', 'feedback — improves future classification', and 'optimize_rollback — Rollback a specific optimization change'. It does not fully describe reversibility or side effects (e.g., what website_setup changes), so 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.

Conciseness4/5

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

The description is front-loaded with a purpose sentence and then a well-structured Args list; each action and parameter line earns its place. It is long due to the genuine complexity of ten actions, but there is little redundancy; a 5 would require tighter grouping or more compact phrasing.

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?

For a high-complexity multi-action tool, the description covers all actions and parameters and cooperates with the annotations and output schema. It does not describe per-action return values or explicitly say which sibling tools to prefer, but the output schema likely covers returns and the action list is sufficiently complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description carries the full burden for parameter semantics, and it succeeds. Every parameter is explained: action has enumerated values with per-action meaning, signal_type has different meanings for 'show' vs 'optimize_history', status has example values, limit/days have defaults, and signal_id/feedback are scoped to specific actions.

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 opens with 'View and analyze buying signals from LinkedIn' and then enumerates ten distinct actions, giving a clear verb+resource map and concrete sub-purposes. It does not reach 5 because the opening phrase understates the mutation/optimization actions (optimize, rollback, website_setup) and does not differentiate this tool from siblings like profile_signals or analytics.

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 provides clear context for when to use each internal action via the Args list, including which parameters apply to which action (e.g., signal_id for feedback and optimize_rollback, days for report, signal_type variants for show vs optimize_history). It lacks explicit alternatives or when-not-to-use guidance relative to sibling tools, so it falls short of a 5.

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