Get the syntax reference entry for one diagram type: description, a render-verified example, and the help page URL. Use it to ground diagram generation.
| Name | Required | Description | Default |
|---|---|---|---|
| type | Yes | Type id or label, e.g. "sequence", "class", "mindmap", "gantt", "json" |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description must convey behavior. It communicates that this is a read-only lookup returning a reference entry, which is adequate. It does not disclose edge cases, error behavior, or what happens for unknown types, but for a simple retrieval tool this is a minimum viable disclosure.
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 two short sentences with no filler. The primary function and return payload are front-loaded, and the usage directive earns its place by connecting to diagram generation.
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 one-parameter lookup tool with no output schema, the description is complete: it names the resource scope, what the result contains, and the intended use. No critical operational detail is missing.
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 100%, so the single parameter 'type' is already documented with examples. The description adds no new parameter semantics beyond reinforcing that the lookup is for one diagram type, so the baseline of 3 is appropriate.
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 ('Get') and resource ('syntax reference entry for one diagram type'), and enumerates exactly what is returned: a description, render-verified example, and help page URL. This clearly distinguishes it from siblings like render_diagram, validate_diagram, and list_diagram_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 a clear use case: 'Use it to ground diagram generation.' It does not explicitly name alternatives or state when not to use this tool, but the stated context is sufficient for an agent to understand its primary purpose.
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.
