Skip to main content
Glama

create_annotation_queue_item

Queue a trace or observation for review by adding it to an annotation queue, with optional initial status.

Instructions

Add a trace or observation to an annotation queue for review.

object_type: 'TRACE' or 'OBSERVATION'. status: optional initial status (defaults to PENDING).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusNo
projectNo
queue_idYes
object_idYes
object_typeYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations provided, the description must carry the burden of behavioral disclosure. It adds some context by specifying object_type values ('TRACE' or 'OBSERVATION') and stating a status default, but the stated default (PENDING) contradicts the schema's default (null). No permissions, side effects, or return behavior are disclosed, which is a gap for a create operation.

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 short and front-loaded with the primary purpose, followed by parameter notes. The structure is efficient, but the inaccurate status default and missing project documentation reduce the value of the second sentence. It remains concise and well-organized overall.

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?

For a mutation tool with no annotations, the description is incomplete. It fails to explain the project parameter, contains a schema contradiction, and omits any mention of return values or prerequisites. While an output schema exists, it does not excuse the lack of behavioral context and incomplete parameter semantics.

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?

The description adds meaning for object_type (enum-like values) and status (optional, though the default is incorrectly stated as PENDING vs null). However, queue_id, object_id, and project are not explained, and the schema has 0% coverage, so the description needed to compensate much more. The contradiction on status defaults further undermines the parameter guidance.

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 clearly states the action ('Add') and the resource ('trace or observation to an annotation queue'), making the tool's purpose immediately understandable. It also distinguishes itself from sibling tools like create_annotation_queue by specifying it adds an item to a queue.

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 such as create_annotation_queue or create_annotation_queue_assignment. There is no mention of exclusions or prerequisites, leaving the agent to infer usage solely from the tool name and siblings.

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/DrishtantKaushal/LangfuseMCP'

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