Freelance MCP Server

optimize_profile

Generate AI-driven profile optimization recommendations for freelancers using profile ID and optional target niche to improve visibility and attract relevant gigs.

Instructions

Provide profile optimization recommendations using LLM analysis

Args:
    profile_id: ID of the profile to optimize
    target_niche: Specific niche to optimize for (optional)

Input Schema

TableJSON Schema

Name	Required	Description	Default
`profile_id`	Yes
`target_niche`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.1.0Sep 13, 2026

TDQS

B3.1/5.0

Behavior2/5

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

No annotations are provided, so the description carries full behavioral burden. It mentions LLM analysis but does not disclose side effects, permissions, rate limits, or whether the profile is modified; 'recommendations' hints at read-only but remains ambiguous.

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 front-loaded with the purpose and then lists the two arguments compactly. Every sentence earns its place, with no redundant or vague filler.

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

Completeness2/5

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

Output schema exists, so return values need not be explained. However, with no annotations and no usage guidance, the description is incomplete for safe invocation: an agent cannot tell when to choose this tool or what behavioral constraints apply.

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?

Schema description coverage is 0%, so the description must compensate. It meaningfully explains both parameters: profile_id as the ID of the profile to optimize, and target_niche as the optional specific niche to optimize for.

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 ('Provide') and resource ('profile optimization recommendations') and adds method ('using LLM analysis'). Clear enough to understand the tool, but it does not distinguish itself from sibling tools like analyze_profile_fit or get_smart_recommendations.

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

Usage Guidelines2/5

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

The description gives no guidance on when to use this tool versus alternatives, nor any prerequisites or exclusions. It only lists arguments, leaving usage context entirely implied.

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

Deploy Server