Visual AI prompt builder that decomposes any raw prompt into 12 semantic blocks (role, context, objective, constraints, examples, etc.) and recompiles them into Claude-optimized XML. Exposes decompose_prompt and compile_prompt tools.
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Average 4.2/5 across 3 of 3 tools scored.
Each tool has a unique role: decompose_prompt converts raw text to structured blocks, compile_prompt converts blocks to XML, and list_block_types provides reference metadata. No two tools serve the same function.
All tool names follow a clear verb_noun pattern with snake_case: compile_prompt, decompose_prompt, list_block_types. The verbs are distinct and descriptive.
With three tools, the server covers the essential workflow of decomposition, compilation, and type reference without redundancy. This is an appropriately scoped toolkit for a focused purpose.
The toolkit provides the full pipeline from raw prompt to compiled XML, plus the necessary reference for known block types. There are no obvious missing operations for the intended workflow.
compile_promptAInspectCompile a list of blocks into a Claude-optimized structured XML prompt.
Takes the JSON returned by decompose_prompt (or manually crafted blocks)
and produces a ready-to-use XML prompt with a token estimate.
Args:
blocks_json: JSON-stringified list of blocks.
Each block: {"type": "role|objective|...", "content": "...",
"label": "...", "description": "...", "summary": ""}
Returns:
The compiled XML prompt with token estimate.
| Name | Required | Description | Default |
|---|---|---|---|
| blocks_json | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the transformation and token estimate, but does not disclose potential side effects, permissions, error handling, or the fact that it is a pure read-only operation. For a compile tool, this is a notable gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is well-structured and concise: a one-sentence purpose, a short explanation, and a clear Args/Returns breakdown. No unnecessary wording.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With one parameter and an output schema, the description adequately covers both input and output. It could enumerate valid block types explicitly, but the reference to decompose_prompt and the partial list ('role|objective|...') gives enough context, and sibling list_block_types covers that.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description provides a detailed structure for blocks_json, listing each block's fields (type, content, label, description, summary) and explaining it is a JSON string. This vastly exceeds the minimal schema ('type: string') and adds essential meaning for correct usage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description starts with a specific verb 'Compile' and resource 'list of blocks', clearly distinguishing it from siblings like decompose_prompt and list_block_types. It explicitly states the output is a Claude-optimized XML prompt with token estimate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states it takes JSON returned by decompose_prompt, implying it's used after decomposition. However, it does not explicitly contrast with alternatives or provide when-not-to-use guidance, so there is clear context but no exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
decompose_promptAInspectDecompose a raw prompt into structured blocks (role, objective, context, constraints, etc.).
Uses AI (Claude/OpenAI) if an API key is configured on the server, otherwise
falls back to keyword-based heuristic analysis.
Returns a JSON list of blocks ready to edit or pass to compile_prompt.
Args:
prompt: The raw prompt string to decompose.
Returns:
A summary of extracted blocks + the full JSON to pass to compile_prompt.
| Name | Required | Description | Default |
|---|---|---|---|
| prompt | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full transparency burden. It discloses the dual-mode operation (AI vs heuristic) and the return format (summary + JSON). It does not cover error cases or side effects, but for a decomposition tool, the behavior is well-communicated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is focused and well-structured with a short summary and explicit Args/Returns sections. It avoids fluff, though the returns section could be more precise about the output shape.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (one parameter) and that an output schema exists, the description provides sufficient workflow context and behavioral notes. It could mention input size limits or API key requirements, but overall it is complete for the complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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. The Args section states 'prompt: The raw prompt string to decompose', adding minimal context beyond the parameter name. For a single self-explanatory parameter, this is acceptable but not rich.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the specific action and resource: 'Decompose a raw prompt into structured blocks' with examples of block types. This clearly distinguishes it from siblings (compile_prompt assembles, list_block_types enumerates).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It provides usage context by mentioning the tool's output is 'ready to edit or pass to compile_prompt', indicating a workflow. It also explains the fallback heuristic mode when no API key is configured. However, it does not explicitly state when to avoid this tool or compare with alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_block_typesAInspectList all available block types in flompt with their descriptions.
Useful to know which types to use when manually crafting blocks
to pass to compile_prompt.
Returns:
Description of each block type and the recommended canonical ordering.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It discloses the output behavior by stating it returns descriptions of each block type and the recommended canonical ordering. For a read-only list operation, this is sufficient, though it does not explicitly affirm that the tool has no 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is tightly structured: a one-sentence purpose, a single usage note, and a clear 'Returns' section. Every sentence adds value, and the most important information (what the tool lists) is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity, zero parameters, and the presence of an output schema, the description is complete enough. It covers what the tool does, when to use it, and what it returns, without requiring deeper elaboration.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, and the baseline for zero-parameter tools is 4. The description adds no parameter-specific semantics, but none are needed since the tool takes no arguments and its purpose is clearly self-contained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific action and resource: 'List all available block types in flompt with their descriptions.' This clearly states what the tool does and distinguishes it from sibling tools like compile_prompt and decompose_prompt, which operate on blocks rather than listing them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states when to use the tool: 'Useful to know which types to use when manually crafting blocks to pass to compile_prompt.' This gives clear context for the tool's role, though it does not mention explicit alternatives or when not to use it.
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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