Placed MCP Server

analyze_offer

Compare a job offer to market rates in your location and receive actionable recommendations for negotiation.

Instructions

Analyze a job offer against market rates and provide recommendations.

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": {
      "company": "<company>",
      "position": "<position>",
      "base_salary": "<base_salary>",
      "location": "<location>"
    },
    "name": "analyze_offer"
  }
}

Input Schema

Name	Required	Description
`company`	Yes	Company name
`position`	Yes	Job title
`base_salary`	Yes	Base salary offer
`location`	Yes	Job location
`bonus`	No	Bonus amount (optional)
`equity`	No	Equity details (optional)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.1.0May 8, 2026

TDQS

B3.4/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden. It only states 'analyze' and 'provide recommendations', but does not disclose whether the tool is read-only, requires authentication, has rate limits, or modifies any data. This lack of behavioral detail is insufficient.

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 a single sentence with no wasted words. It front-loads the purpose effectively.

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?

Given 6 parameters, no output schema, and no annotations, the description is too brief. It does not explain the output format, what recommendations entail (e.g., salary range, negotiation tips), or how the analysis is performed. This leaves significant gaps for an AI agent.

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 coverage is 100%, so baseline is 3. The description adds meaning by stating the tool compares against 'market rates', implying parameters like location and position are used for market comparison. This adds context beyond the schema's parameter descriptions.

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 verb 'analyze', the resource 'job offer', and the actions 'against market rates' and 'provide recommendations'. It effectively distinguishes from sibling tools like 'get_market_comp' or 'generate_salary_negotiation_script' by combining analysis and 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 provides no guidance on when to use this tool versus alternatives such as 'get_company_salary_data' or 'generate_salary_negotiation_script'. It does not mention prerequisites or context where this tool is appropriate.

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

Deploy Server