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ffucucuoglu

linkfetch-mcp

by ffucucuoglu

linkfetch_safety_status

Check LinkedIn safety budget usage before bulk outreach to see per-action used vs limits, reset times, account tier, warm-up week, and any pause.

Instructions

Show how much of the user's LinkedIn safety budget is used: per action (connection requests, messages, profile views, searches, comments…) used vs. limit per minute/hour/day/week, when each window resets, the account's tier and warm-up week, and any pause LinkedIn triggered. Call before planning bulk outreach so you can tell the user what fits today.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
account_urnNoThe LinkedIn account's own urn:li:fsd_profile:<id>. Omit to list every account on this API key.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A3.9/5.0
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 discloses the return contents in detail (per-action used vs. limit, window reset times, tier, warm-up week, LinkedIn-triggered pauses) and implies a non-mutating read. It stops short of stating permission/auth requirements or explicitly labelling itself read-only.

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?

Two sentences, with the return-content summary front-loaded and the usage trigger second. The first sentence is dense with an enumerated list but every element is informative rather than filler.

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?

With no output schema and no annotations, the description does the necessary work of enumerating what comes back and when to call it. It is nearly complete for a read-only status tool; only the absence of any auth/permission note leaves a small gap.

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 single account_urn parameter is fully documented in the schema (100% coverage), including the omit-to-list-all behavior, so the description adds no meaning beyond it. Baseline 3 is correct when the schema does the heavy lifting.

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?

States a specific verb+resource ('Show how much of the user's LinkedIn safety budget is used') and enumerates the tracked actions, so the agent knows exactly what this returns. It does not explicitly contrast itself with the nearby siblings linkfetch_safety_limits and linkfetch_safety_resume, which is the only thing keeping it from a 5.

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?

Gives an explicit usage trigger: 'Call before planning bulk outreach so you can tell the user what fits today.' That is clear context for when to reach for it, but it names no alternatives and states no when-not condition (e.g., how it differs from safety_limits).

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