Freelance MCP Server

negotiate_rate

Generate a rate negotiation strategy and message using current and target rates, project complexity, and justification points to help freelancers negotiate effectively.

Instructions

Generate rate negotiation strategy and message using Langchain ChatGroq

Args:
    current_rate: Current offered rate
    target_rate: Desired rate
    project_complexity: Complexity level (low, medium, high)
    justification_points: List of points to justify higher rate

Input Schema

TableJSON Schema

Name	Required	Default
`target_rate`	Yes
`current_rate`	Yes
`project_complexity`	No	medium
`justification_points`	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 the full behavioral burden. It says the tool generates a strategy and message using an LLM, but does not disclose side effects, authentication needs, latency/cost implications, or whether it is read-only. It adds little beyond the basic generation concept.

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?

The purpose is front-loaded in the first line, followed by a structured Args block. It is appropriately sized for a four-parameter tool, though the Langchain ChatGroq detail is implementation noise that could be omitted.

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 four-parameter tool with no annotations and an output schema, the description covers purpose and all parameters adequately. However, it lacks usage context, edge cases, and any behavioral notes about the LLM-backed generation. It is minimally complete but has clear gaps in routing guidance.

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, and it does by documenting all four parameters in an Args block. It clarifies each field's meaning, including that project_complexity accepts low/medium/high and justification_points is a list. Units or format constraints for the rate fields are still absent, preventing a 5.

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: generate a rate negotiation strategy and message. It is distinguishable from siblings like calculate_pricing_strategy or generate_proposal, but does not explicitly name alternatives. The implementation note about Langchain ChatGroq is extra but not harmful.

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?

No when-to-use guidance, no prerequisites, and no alternatives are provided. The description only lists arguments, leaving the agent to infer when this tool is appropriate versus calculate_pricing_strategy or generate_proposal.

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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