prompt-improver-mcp

Analyze a prompt

analyze_prompt

Profile a prompt’s task type and list the improvement techniques that would apply, with citations, without rewriting the prompt.

Instructions

Profile a prompt's task type and list the techniques improve_prompt would apply, with citations, without rewriting anything.

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": {
      "prompt": "<prompt>"
    },
    "name": "analyze_prompt"
  }
}

Input Schema

Name	Required	Description	Default
`prompt`	Yes	The prompt to analyze, verbatim.
`target`	No		reasoning
`context`	No
`history`	No
`has_image`	No

Output Schema

Name	Required	Description
`profile`	Yes
`techniques`	Yes
`inference_tips`	Yes	Inference-time techniques to consider; not part of the prompt text.

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 15, 2026

TDQS

A4/5.0

Behavior4/5

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

With no annotations, the description carries the full behavioral burden and does disclose the key trait: it is a non-mutating inspection ('without rewriting anything') that produces cited output. It does not mention the effect of the 'target'/'has_image' inputs on behavior or any limits, so it is good but not exhaustive.

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?

A single front-loaded sentence that names the action, the output, and the key constraint with zero filler. Every clause carries information.

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 existence of an output schema means return values need not be explained, and the non-mutation nature is covered. However, four of five parameters lack any semantic guidance and no annotations exist, so the agent lacks enough to confidently populate target/context/history/has_image.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 20% across 5 parameters, so the description must compensate and largely does not. It says nothing about 'target', 'context', 'history', or 'has_image', leaving the agent to guess how targeting the analysis or supplying conversation history changes the result.

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 states a specific verb and resource ('Profile a prompt's task type') plus the concrete artifact it returns ('list the techniques improve_prompt would apply, with citations'). It explicitly differentiates itself from its closest sibling by adding 'without rewriting anything', so an agent can distinguish it from improve_prompt without opening either schema.

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

Usage Guidelines4/5

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

'without rewriting anything' and the reference to improve_prompt imply the usage context: inspect/analyze before committing to a rewrite. It names the sibling tool, but it never states explicit when-to-use/when-not conditions or prerequisites (e.g. when to choose this over calling improve_prompt directly), leaving the routing partly to inference.

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

Deploy Server