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LinkedIn Profile Optimizer

LinkedIn Profile Optimizer

Lint a profile field

lint_field
Read-only

Check a LinkedIn field against its character limit and flag AI-sounding text, list markers, and emoji. See what stays visible before truncation.

Instructions

Check one LinkedIn field against its character limit and for AI-sounding text (em dashes, stock phrases, 'it's not X, it's Y'), list markers and emoji. Shows what is visible before truncation.

Input Schema

NameRequiredDescriptionDefault
textYesThe exact text of the field
fieldYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.5/5.0
Behavior3/5

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

The readOnlyHint annotation already establishes that this is a safe inspection tool. The description adds useful behavioral detail by naming the lint categories and noting that it shows what is visible before truncation, but it does not describe the result format, severity, or whether it suggests fixes.

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 two sentences, front-loads the main action, and every clause contributes specific information about what is checked. There is no repetition or wasted wording.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a low-complexity, read-only tool with no output schema, the description covers purpose and lint scope adequately. It still leaves gaps around the returned output and sibling differentiation, and it does not compensate for the undocumented field enum.

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?

Schema coverage is 50%: the text parameter is described, but the field parameter's enum lacks a schema description. The description clarifies that the field is a LinkedIn field and that text is checked against its limit, but it does not explain how the enum values differ or how field choice affects limits and checks.

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?

The description gives a specific verb ('Check') and resource ('one LinkedIn field'), and it enumerates the checks: character limit, AI-sounding text, list markers, and emoji. It is clear what the tool does, but it does not explicitly distinguish itself from siblings such as review_headline or review_experience.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied by the mention of checking a field and showing visible text before truncation, which suggests pre-post or pre-review validation. However, there is no explicit when-to-use guidance or statement about when to choose this over sibling tools like review_profile or keyword_coverage.

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