Placed MCP Server

improve_bullet_point

Rewrite a single bullet point to enhance its impact and ensure ATS compatibility for your resume.

Instructions

Improve a single bullet point with AI to make it more impactful and ATS-friendly.

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": {
      "bullet_point": "<bullet_point>"
    },
    "name": "improve_bullet_point"
  }
}

Input Schema

Name	Required	Description	Default
`bullet_point`	Yes	Current bullet point text
`context`	No	Context (job title, company, role)

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.1.0May 8, 2026

TDQS

C2.9/5.0

Behavior2/5

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only states 'improve' without detailing the transformation (e.g., rewriting style, length constraints, whether it preserves original meaning), nor does it mention if the tool is read-only or mutates state. This leaves uncertainty about side effects.

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 description is a single concise sentence with no unnecessary words. It is front-loaded with the action and purpose. However, it could benefit from a slightly more structured format (e.g., listing key behaviors or output).

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 simple tool with two parameters and no output schema, the description is minimally adequate. It explains the what and why but not the how or the output format. Given the complexity, it is complete enough but lacks details about the result (e.g., the improved bullet point text) that an agent might need.

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 100%, so the input schema already describes both parameters. The description adds no additional semantic meaning beyond that, such as how 'context' influences the improvement. Baseline score of 3 is appropriate as the schema does the heavy lifting.

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 clearly states the tool improves a single bullet point to make it more impactful and ATS-friendly. It uses a specific verb and resource, and while there are many resume-related siblings, this one is distinctly for individual bullet points. However, it could more explicitly differentiate from tools like optimize_resume_section.

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 optimize_resume_section or optimize_resume_for_job. There are no usage contexts, exclusions, or hints about prerequisites.

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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