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Yandex Tracker MCP

queue_get_local_fields

Retrieve queue-specific custom fields for a Yandex Tracker project by specifying its Queue ID. Simplifies access to localized field data for efficient issue management.

Instructions

Get local fields for a specific Yandex Tracker queue (queue-specific custom fields)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queue_idYesQueue (Project ID) to search in, like 'SOMEPROJECT'

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The async handler function that implements the core logic of the queue_get_local_fields tool, calling the underlying queues_get_local_fields method.
    async def queue_get_local_fields(
        ctx: Context[Any, AppContext],
        queue_id: QueueID,
    ) -> list[LocalField]:
        if (
            settings.tracker_limit_queues
            and queue_id not in settings.tracker_limit_queues
        ):
            raise TrackerError(f"Queue `{queue_id}` not found or not allowed.")
    
        fields = (
            await ctx.request_context.lifespan_context.queues.queues_get_local_fields(
                queue_id,
                auth=get_yandex_auth(ctx),
            )
        )
        return fields
  • The call to register_tools(settings, mcp) which defines and registers the queue_get_local_fields tool among others using @mcp.tool decorators.
    register_tools(settings, mcp)
  • Type definition for the input parameter 'queue_id' using Pydantic Annotated with description, providing input schema validation.
    QueueID = Annotated[
        str,
        Field(
            description="Queue (Project ID) to search in, like 'SOMEPROJECT'",
        ),
    ]
  • The @mcp.tool decorator that registers the queue_get_local_fields function as an MCP tool with its description.
    @mcp.tool(
        description="Get local fields for a specific Yandex Tracker queue (queue-specific custom fields)"
    )
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. It states the tool retrieves data ('Get'), implying a read-only operation, but doesn't specify whether it requires authentication, has rate limits, returns paginated results, or what happens with invalid inputs. For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior and constraints.

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 a single, efficient sentence that front-loads the core purpose without unnecessary details. It uses clear terminology ('queue-specific custom fields') and avoids redundancy, making it easy to parse. Every word contributes directly to understanding the tool's function, with zero waste.

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?

Given the tool has an output schema (which covers return values), no annotations, and a simple input schema with full coverage, the description is minimally adequate. It states what the tool does but lacks context on usage, behavioral traits, or integration with siblings. For a read operation with output schema support, it meets basic needs but doesn't provide enough guidance for optimal agent decision-making in complex scenarios.

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?

The input schema has 100% description coverage, with the 'queue_id' parameter clearly documented as 'Queue (Project ID) to search in, like 'SOMEPROJECT''. The description adds minimal value beyond this, only reiterating that it's for a 'specific Yandex Tracker queue'. No additional semantics, such as format examples or constraints, are provided, so the baseline score of 3 is appropriate given the schema does the heavy lifting.

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's purpose: 'Get local fields for a specific Yandex Tracker queue (queue-specific custom fields)'. It specifies the verb ('Get'), resource ('local fields'), and context ('Yandex Tracker queue'), distinguishing it from sibling tools like 'get_global_fields' by emphasizing queue-specific fields. However, it doesn't explicitly differentiate from all siblings, such as 'queue_get_tags' or 'queue_get_versions', which also retrieve queue-specific data.

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 when to choose it over 'get_global_fields' for global versus queue-specific fields, or when to use it in conjunction with other tools like 'issue_get' or 'issues_find'. There's no context on prerequisites, such as needing a valid queue ID, or exclusions, leaving the agent to infer usage from the tool name and parameters alone.

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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