Freelance MCP Server

calculate_pricing_strategy

Determine optimal gig pricing using AI by analyzing required skills, your rate range, and success rate to recommend a tailored pricing strategy.

Instructions

Calculate optimal pricing strategy for a specific gig using AI

Args:
    gig_id: ID of the gig
    skills: Your skills
    user_rate_min: Your minimum hourly rate
    user_rate_max: Your maximum hourly rate
    success_rate: Your historical success rate (0-100)

Returns:
    Optimal pricing recommendation with strategy

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "gig_id": "<gig_id>",
      "skills": "<skills>"
    },
    "name": "calculate_pricing_strategy"
  }
}

Input Schema

Name	Required	Description	Default
`gig_id`	Yes
`skills`	Yes
`success_rate`	No
`user_rate_max`	No
`user_rate_min`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.1.0Sep 13, 2026

TDQS

B3.2/5.0

Behavior2/5

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

With no annotations provided, the description must carry the full behavioral burden, but it only states that the tool calculates a recommendation using AI and returns an optimal pricing recommendation. It does not disclose side effects, rate limits, auth requirements, or whether the operation is 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, followed by a clear Args and Returns structure. Every sentence is informative and there is no redundant or wasted text.

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?

The description covers all parameters and gives a high-level return statement, which is helpful given the 0% schema coverage. However, with no annotations, it should provide more behavioral context (e.g., safety, side effects) and usage guidance to fully support correct invocation.

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 does list all five parameters with brief explanations, including the 0-100 range for success_rate, which adds meaning beyond the schema. However, it does not clarify formats or types for some parameters (e.g., skills as an array).

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 states a specific verb ('Calculate') and resource ('pricing strategy') for a specific gig using AI. It is clear what the tool does, but it does not differentiate itself from sibling tools like negotiate_rate or analyze_skill_demand.

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 does not explain when to use this tool versus alternatives, nor does it provide any prerequisites or exclusions. Usage is only implied by the purpose statement, leaving the agent to infer context.

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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