preset-mcp

list_annotation_layers

Retrieve annotation layers for overlaying time-based markers on charts. Filter by name or choose response detail level.

Instructions

List annotation layers in the current workspace.

Annotation layers let you overlay time-based markers on charts (e.g. deploys, incidents). Use this to discover layer IDs.

Args: response_mode: 'compact' (id+name), 'standard' (key fields), or 'full' (raw API response). Default: standard. name_contains: Case-insensitive substring filter on layer name.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_annotation_layers"
  }
}

Input Schema

Name	Required	Description	Default
`response_mode`	No		standard
`name_contains`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.7.2Jun 8, 2026

TDQS

B3.3/5.0

Behavior2/5

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

No annotations are provided, so the description must cover behavioral traits. It does not mention whether results are paginated, if the operation is read-only, or any side effects. The description only states the basic functionality.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise at about 100 words, with a clear opening sentence, a brief definition of annotation layers, and an Args section. Every sentence serves a purpose.

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

Completeness3/5

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

The tool has an output schema (not shown), so the description need not detail return values. However, it could mention that the output varies by response_mode. Overall, it covers the essential context for usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description adds significant value by explaining each parameter: response_mode's enum options (compact, standard, full) with their meanings, and name_contains as a case-insensitive substring filter.

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 clearly states the tool lists annotation layers in the current workspace, explaining what annotation layers are and that the tool helps discover layer IDs. This distinguishes it from the sibling get_annotation_layer, though not explicitly mentioned.

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

Usage Guidelines3/5

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

The description notes 'Use this to discover layer IDs,' implying the use case, but does not contrast with alternatives like get_annotation_layer or specify when not to use this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server