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Destructive

Read new LinkedIn replies and classify them as positive, negative, or questions, then prioritize the people worth answering first. Updates outreach statuses and respects opt-outs.

Instructions

Read new LinkedIn replies and record what they mean.

Fetches new messages, classifies each reply (positive, negative, question)
and lists the people worth answering first.

It sends nothing, but it does write: it marks an invitation accepted once
the person answers or connects, moves an outreach to replied, stores the
messages, and sets opted-out when someone asks not to be contacted, which
stops all future outreach to them. It is the only path that notices an
accepted invitation, so follow-ups depend on it having run.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

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

A4.7/5.0

Behavior5/5

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

The description goes well beyond the annotations by disclosing concrete side effects: marking invitations accepted, moving outreach to replied, storing messages, and setting opted-out in a way that stops all future outreach. It also explicitly states it sends nothing, which supports the readOnlyHint=false and destructiveHint=true annotations without contradicting them.

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 compact but information-dense: a one-sentence summary, a functional overview, and then the important side-effect and dependency details. Nothing is redundant, and the most critical behavioral constraints are front-loaded.

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 parameterless tool with an output schema, the description fully covers what the tool does, what it changes, what it does not do, and why it matters for follow-ups. The dependency warning about accepted invitations is especially valuable context an agent needs before invoking it.

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

Parameters4/5

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

The tool has zero parameters, so the schema carries no parameter semantics burden and the baseline is 4. The description notes no arguments and instead focuses on behavior, which is entirely appropriate for a parameterless tool.

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 opens with a specific verb and resource: 'Read new LinkedIn replies and record what they mean.' It then details what it fetches, classifies, and lists, and distinguishes itself from siblings by noting it sends nothing and is the only path that notices accepted invitations.

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 gives clear context: this tool reads, records, classifies, and prioritizes replies, and is the only path that detects accepted invitations, making it essential for follow-ups. It does not explicitly name alternative tools or when-not-to-use conditions, so it stops short of full routing guidance.

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