Skip to main content
Glama

Get annotated message

get_annotated_message

Retrieve content blocks carrying priority and audience annotations for error, success, or debug messages. Optionally include images to provide additional context.

Instructions

Return content blocks carrying priority and audience annotations.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
message_typeYes
include_imageNo
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. 'Return content blocks' implies a read-only operation, but the description does not explain how message_type affects results, what include_image changes, or what happens when no annotations exist. This is a meaningful transparency gap for an unannotated tool.

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 a single sentence with no wasted words and starts with the operative verb 'Return'. It is concise, though it could have used its brevity to include parameter or usage context without becoming bloated.

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 is structurally simple (two scalar parameters, no output schema, no nested objects), so the required effort is modest. The schema supplies the required enum and default, and the description names the return content, but the lack of any usage guidance and the 0% parameter coverage leave an agent with only partial context for invoking it well.

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

Parameters2/5

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

Schema description coverage is 0%, so the description needed to compensate, but it mentions neither message_type nor include_image. The parameter names and enum values are somewhat self-explanatory, but the description adds no nuance about filtering by message type or the image inclusion behavior.

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 names a concrete verb (return) and a specific resource (content blocks with priority and audience annotations), so the core purpose is clear. It does not explicitly position itself against sibling tools like get_resource_reference or get_resource_links, so it falls just short of full differentiation.

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 gives no guidance on when to use this tool instead of siblings, and it does not state any exclusions or alternative tool names. An agent must infer usage entirely from the name and schema.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/bigg01/aipod'

If you have feedback or need assistance with the MCP directory API, please join our Discord server