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linkedin-buying-signals

Find new leads from signal topics

find_leads_from_signals

Search recent LinkedIn posts on the topics of this account's own signal definitions, keep only posts a check can prove are on topic, and return the people who wrote, commented on or reacted to them, filtered against the ICP (role, industry, country) and ranked. Nothing is saved: pass the returned candidates to import_sourced_leads. Spends a little of the monthly sourcing budget.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
signalTypeNoWhich signal to source for; omit to use the first enabled one (see list_signals)
postsPerSignalNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Annotations only note readOnlyHint=false and destructiveHint=false; the description adds the concrete side effect of spending monthly sourcing budget and clarifies that no data is saved. It also hints at a verification step ('a check can prove are on topic'). This goes beyond annotations and is consistent with them—no contradiction.

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?

Four tight sentences front-load the main action, then add scope and side-effect details. Zero filler; every sentence earns its place. The structure leads with the core behavior and ends with the budget note.

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?

For a tool with only two optional parameters and no output schema, the description provides all needed context: what it returns (people who wrote/commented/reacted), the filtering criteria (ICP), the non-persistent nature, and the follow-up tool. Nothing essential for an agent to call it correctly is missing.

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

Parameters2/5

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

Schema coverage is only 50% (postsPerSignal lacks a description). The tool description does not explain postsPerSignal at all, leaving its meaning to the default and name. signalType is only implicitly connected to 'signal definitions'. The description fails to compensate for the low schema coverage, so parameter semantics are inadequately communicated.

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 states a specific verb+resource: search LinkedIn posts on signal topics, filter against ICP, and return people. It clearly distinguishes itself from siblings like import_sourced_leads by specifying that nothing is saved and candidates should be passed there. This is a precise, non-tautological purpose.

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?

It gives explicit workflow guidance ('pass the returned candidates to import_sourced_leads') and notes the budget cost. It does not name alternative tools or state when not to use it, but the use case is clear enough. Lacking explicit exclusions keeps it from a 5.

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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