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trident

by io.github.alexeibespalov

make_suggestion

Posts a visible suggestion to human collaborators watching the document WITHOUT making any changes to the diagram. Use this when you observe something worth flagging — a potential mistake, an improvement, or a question — but want the human to decide. The suggestion appears as a bold "SUGGESTION:" callout in the UI with a radar pulse animation on your presence dot for 10 seconds. Does NOT mutate the diagram. Requires a valid viewer or editor access token.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tokenYesViewer or editor access token for this document.
agentIdYesYOUR AI assistant name (e.g. "Claude.ai", "GitHub Copilot"). Shown as "[agentId] working with [userName]" in the collaboration presence dot.
entityIdNoID of the node or container this suggestion relates to. Always provide this when the suggestion concerns a specific element — it moves the AI cursor to that element so collaborators know exactly what you are referring to.
userNameYesFirst name of the human you are assisting.
suggestionYesThe suggestion text to show to human collaborators (max 500 chars). Be concise and actionable.

TDQS

A4.5/5.0
Behavior5/5

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

The description adds significant behavioral details beyond annotations: the suggestion appears as a visible 'SUGGESTION:' callout with a radar pulse animation on the presence dot for 10 seconds, and it does not mutate the diagram. This complements the annotations (e.g., destructiveHint=false) and helps the agent understand the impact on the UI.

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?

The description is compact (roughly three sentences), front-loaded with the core purpose, and includes only necessary details. Each sentence contributes value—purpose, usage context, and behavioral transparency—without redundancy or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a relatively simple tool with no output schema, the description covers the essential context: what it does, when to use it, how it appears to collaborators, and the required access token. The parameter schema handles field-level details, so no additional context is needed.

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?

The input schema has 100% description coverage for all parameters, including detailed meanings for token, agentId, entityId, userName, and suggestion. The tool description itself does not add parameter-specific semantics beyond what the schema already provides, so the baseline score of 3 is appropriate.

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 ('posts'), a clear resource ('a visible suggestion to human collaborators'), and a key distinguishing constraint ('WITHOUT making any changes to the diagram'). It clearly differentiates this from sibling tools that modify the diagram.

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?

The description explicitly states when to use this tool ('when you observe something worth flagging — a potential mistake, an improvement, or a question') and clarifies that it is for flagging rather than acting. It does not explicitly name alternative tools or provide when-not-to-use scenarios, but the 'WITHOUT making changes' implies 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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TDQS

A4.3/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: CRUD operations for nodes, containers, connections, and annotations are separated, and read-only/utility tools like open_document, get_document_summary, and explain are well-differentiated. No two tools have overlapping functionality.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g. add_node, delete_connection, get_document_summary). Even utility tools like open_document and validate_trident adhere to this pattern, making the set predictable and easy to navigate.

Tool Count4/5

With 22 tools, the server is slightly above the typical well-scoped range (3-15). Each tool serves a specific purpose, covering CRUD for diagram elements, multiple read operations, and collaboration features, but the count feels a bit heavy for a focused diagramming tool.

Completeness4/5

The tool set provides comprehensive CRUD for core elements (nodes, containers, connections, annotations), multiple read methods, guides, and collaboration aids. Minor gaps exist: no bulk operations, no tool to create or manage documents themselves, but the core editing workflow is well-covered.

Resources