Check PlantUML source without rendering. Returns {ok:true, diagram_type} or {ok:false, error:{code, message, line, column, source_line, hint, diagram_type_detected, help}}. Cheap - call it in a generate -> validate -> fix loop before rendering.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden, and it delivers substantial behavioral detail: it is non-rendering, cheap, and it documents both success and error return shapes including nested fields like line, column, source_line, and hint. This gives the agent a clear model of what will happen and what the output will look like.
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 compact and front-loaded: the primary action appears first, followed by a precise return contract, then a practical usage tip. Every sentence contributes unique value and there is no redundant or filler content.
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?
For a single-parameter validation tool with no output schema and no annotations, this description is complete. It provides the return contract on success and failure, explains the error structure, signals performance characteristics, and recommends a workflow. An agent has enough information to invoke it correctly and interpret its results.
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 one parameter, text, with 0% description coverage in the schema itself, so the description must compensate. The phrase 'Check PlantUML source' implies the text parameter is the PlantUML source, which is helpful but minimal. It does not clarify expected formatting, whether the full @startuml block is required, or any constraints on input size or encoding.
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 a specific verb and resource: 'Check PlantUML source without rendering.' It clearly distinguishes validate_diagram from render_diagram by emphasizing validation versus rendering, and from list_diagram_types by focusing on checking source rather than enumerating types.
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 gives explicit usage guidance: 'Cheap - call it in a generate -> validate -> fix loop before rendering.' This clearly indicates when to use it and its intended position in a workflow. It doesn't explicitly name what tool to use instead for rendering, but 'without rendering' and 'before rendering' make the alternative obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct responsibility: encoding/decoding tokens, rendering/validating diagrams, and retrieving syntax/reference information. The descriptions explicitly call out potential confusion points (e.g., token alphabet vs base64) and separate render from validate.
All tool names follow a consistent lowercase snake_case verb_noun pattern: decode_token, encode_source, get_syntax_reference, list_diagram_types, render_diagram, validate_diagram. This makes the tool set predictable and easy to navigate.
Six tools is well-scoped for a PlantUML-focused server: two token operations, two diagram lifecycle operations, and two reference/type lookup operations. There is no redundancy and no missing core capability that would require more tools.
The tool set covers the full diagram workflow: picking a type, getting syntax guidance, validating source, rendering output, and encoding/decoding shareable tokens. Agents can complete both common and advanced PlantUML tasks without dead ends.
