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horizonbymuneeb

linkedin-mcp-pro

auto_connect_by_criteria

Find LinkedIn members by role, location, and keywords, then send connection requests with risk controls including daily limits and personalized notes.

Instructions

Find people matching criteria and send connection requests through the safety gate. HIGH BAN RISK: defaults to 20/day, 3/hour, requires personalized note (no blank invites), blocks recruiters/agencies by default. ALWAYS start with dry_run=true.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
roleNoe.g. 'ML Engineer'
locationNoe.g. 'Pakistan'
keywordsNospace-separated keywords
max_resultsNo
Behavior5/5

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

With no annotations, the description fully carries behavioral disclosure. It warns of high ban risk, default rate limits (20/day, 3/hour), requirement for personalized notes, and blocking of recruiters/agencies—critical for safe invocation.

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 extremely concise: two sentences front-load the most critical info (purpose, safety, defaults). Every word contributes value with no redundancy.

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, the description adequately covers input criteria, safety gate, and risk. It lacks details on return format or confirmation, but for a bulk connection tool, the guidance is sufficient.

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?

The input schema covers all parameters with descriptions (100% coverage), so baseline is 3. However, the description mentions a 'dry_run' parameter not present in the schema, which is misleading. This contradiction reduces the score.

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 tool's purpose: 'Find people matching criteria and send connection requests'. It distinguishes itself from siblings like 'send_connection_request' and 'search_people' by being a bulk, safety-gated operation.

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 explicit usage guidance: 'ALWAYS start with dry_run=true' and warns about ban risk, rate limits, and blocked groups. It does not explicitly list when not to use or alternatives, but the context is strong enough for an AI agent to infer cautious use.

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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