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observability-aiops

create_annotation

Destructive

Create Grafana annotations to mark events, attach them to dashboards, add tags, or preview changes with dry-run before recording.

Instructions

[WRITE][risk=medium] Create a Grafana annotation (an event marker).

Args: text: Annotation text. tags: Optional list of tag strings. dashboard_uid: Optional dashboard UID to attach the annotation to. dry_run: If True, preview without creating. target: Grafana target name from config; omit for the default.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
textYes
targetNo
dry_runNo
dashboard_uidNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.7.0

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=false, idempotentHint=false, and destructiveHint=true, so the write/repeat-safety profile is largely covered. The description adds useful context the annotations do not carry — the [WRITE][risk=medium] label and the dry_run "preview without creating" semantics — but never says whether the created annotation can be deleted, what happens if the same text is submitted twice, or how the target config is resolved.

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 purpose is front-loaded in the first sentence, followed by a compact Args block that maps one line per parameter. It is appropriately sized for a five-parameter mutation tool, with no filler sentences.

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

Completeness4/5

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

For a 5-parameter mutation tool with no output schema, the description covers purpose, every argument, and the dry-run escape hatch, which is nearly everything an agent needs to invoke it correctly. The remaining gap is failure/permission behavior, which is not addressed.

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 description coverage is 0%, so the description must carry parameter meaning, and it does: it documents all five arguments, including the optionality of tags/dashboard_uid/target and the behavior of dry_run. It stops short of formats (e.g. tag syntax or how dashboard_uid relates to tags), keeping it below a 5.

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 and resource ("Create a Grafana annotation") and adds a clarifying gloss ("an event marker"). No sibling tool creates annotations, so the operation is unambiguously distinct from the list/get/query siblings that surround it.

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?

Usage is only implied: the agent can infer this is the tool for writing event markers, and the dry_run argument hints at a preview-before-commit workflow. There is no explicit statement of when to use this versus alternatives, nor prerequisites such as required Grafana permissions or target configuration.

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