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mdnaimul22

LinkedIn MCP Pro Max

by mdnaimul22

application

List, track, and update job statuses with notes and URLs. Filter by status to see your pipeline at a glance.

Instructions

Manage tracked job applications locally.

Args: action: 'list', 'track', or 'update', job_id: LinkedIn job ID, job_title: Job title, company: Company name, status: Application status (interested/applied/interviewing/offered/rejected/withdrawn). For 'list', filters results. notes: Optional notes, url: Optional job URL,

allowed_args_for_action = { "list": ["status"], "track": ["job_id", "job_title", "company", "status", "notes", "url"], "update": ["job_id", "status", "notes"] }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlNo
notesNo
actionYes
job_idNo
statusNo
companyNo
job_titleNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses action-specific argument constraints and notes that 'list' filters results by status. This provides useful behavioral context beyond a bare function signature, though it omits details on error handling or overwrite semantics.

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 structured and front-loaded with a one-sentence purpose, followed by a compact Args list and an allowed_args map. Every line is informative; no fluff or 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?

The description covers all actions, parameters, and per-action constraints. It doesn't explain return values, but an output schema exists. Minor gaps remain around edge-case behavior like update on missing records, but overall the description is sufficient for a small tool.

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?

The description adds substantial meaning to each parameter (e.g., job_id as LinkedIn ID, status with allowed values, notes/url as optional) and defines per-action allowed args. This enriches the bare type-only schema, which otherwise has 0% coverage.

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 function—managing tracked job applications locally—and enumerates the three actions (list/track/update). It uses a specific verb 'Manage' and resource 'tracked job applications', distinguishing it from sibling tools like 'job'.

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?

Provides explicit guidance via the allowed_args_for_action mapping, which tells the agent exactly which parameters are valid for each action. This serves as clear usage instructions, though it doesn't explicitly compare to alternative tools or state when not to use this tool.

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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