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kajirita2002

honeycomb-mcp-server

honeycomb_board_create

Generate custom boards in honeycomb-mcp-server by specifying a name, description, and query IDs for organized data visualization and analysis.

Instructions

Create a new board

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
descriptionNoDescription of the board
nameYesName of the board
query_idsNoQuery IDs to include in the board
Behavior2/5

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. 'Create a new board' implies a write operation that likely requires permissions and may have side effects (e.g., creating a persistent resource), but it doesn't specify authentication needs, rate limits, error conditions, or what happens on success (e.g., returns a board ID). For a mutation tool with zero annotation coverage, this lack of behavioral context is a significant gap.

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 extremely concise at three words, front-loading the core action and resource without any wasted text. Every word earns its place by directly conveying the tool's purpose. This efficiency makes it easy for an agent to parse quickly, though it sacrifices detail for brevity.

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

Completeness2/5

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

Given the complexity of a creation tool with no annotations and no output schema, the description is incomplete. It doesn't cover behavioral aspects like authentication requirements, error handling, or return values (e.g., what happens after creation). While the schema covers parameters well, the overall context for safe and effective use is lacking, especially compared to siblings that might have more descriptive names or annotations.

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?

Schema description coverage is 100%, with clear descriptions for name, description, and query_ids parameters in the input schema. The description adds no additional parameter semantics beyond what's already documented in the schema. According to the rules, when schema coverage is high (>80%), the baseline score is 3 even with no param info in the description, which applies here.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Create a new board' clearly states the action (create) and resource (board), making the purpose immediately understandable. It distinguishes from siblings like board_get, board_update, and board_delete by specifying creation rather than retrieval, modification, or deletion. However, it doesn't specify what a 'board' is in the Honeycomb context, which could help differentiate from other creation tools like datasets_create or query_create.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing authentication via honeycomb_auth), when to choose this over board_update for modifications, or how it relates to board_delete or boards_list. Without such context, an agent must infer usage from the name alone, which is insufficient for optimal tool selection.

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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