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Yandex Tracker MCP

get_global_fields

Retrieve all global fields available in Yandex Tracker for managing and customizing issue attributes, ensuring consistent data across workflows.

Instructions

Get all global fields available in Yandex Tracker that can be used in issues

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • MCP tool handler for 'get_global_fields'. Decorated with @mcp.tool and delegates the call to the fields protocol implementation in the app context.
    @mcp.tool(
        description="Get all global fields available in Yandex Tracker that can be used in issues"
    )
    async def get_global_fields(
        ctx: Context[Any, AppContext],
    ) -> list[GlobalField]:
        fields = await ctx.request_context.lifespan_context.fields.get_global_fields(
            auth=get_yandex_auth(ctx),
        )
        return fields
  • Calls register_tools which defines and registers all MCP tools including 'get_global_fields' on the FastMCP server instance.
    mcp = create_mcp_server()
    register_resources(settings, mcp)
    register_tools(settings, mcp)
  • Pydantic model defining the output schema (GlobalField) returned by the get_global_fields tool.
    class GlobalField(BaseTrackerEntity):
        """Global field available in Yandex Tracker"""
    
        id: str
        name: str | None = None
        key: str | None = None
        version: int | None = None
        schema_: FieldSchema | None = Field(
            default=None, alias="schema", serialization_alias="schema"
        )
        readonly: bool | None = None
        options: bool | None = None
        suggest: bool | None = None
        type: str | None = None
        order: int | None = None
        suggestProvider: SuggestProvider | None = None
        optionsProvider: OptionsProvider | None = None
        queryProvider: QueryProvider | None = None
        category: CategoryRef | None = None
  • Core implementation of get_global_fields in TrackerClient: performs HTTP GET to Yandex Tracker API /v3/fields endpoint with authentication.
    async def get_global_fields(
        self, *, auth: YandexAuth | None = None
    ) -> list[GlobalField]:
        async with self._session.get(
            "v3/fields", headers=await self._build_headers(auth)
        ) as response:
            response.raise_for_status()
            return GlobalFieldList.model_validate_json(await response.read()).root
  • Protocol (interface) defining the get_global_fields method signature used by the MCP handler and implementations.
    class GlobalDataProtocol(Protocol):
        async def get_global_fields(
            self, *, auth: YandexAuth | None = None
        ) -> list[GlobalField]: ...
        async def get_statuses(self, *, auth: YandexAuth | None = None) -> list[Status]: ...
        async def get_issue_types(
            self, *, auth: YandexAuth | None = None
        ) -> list[IssueType]: ...
        async def get_priorities(
            self, *, auth: YandexAuth | None = None
        ) -> list[Priority]: ...
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 does not disclose any behavioral traits such as authentication needs, rate limits, or response format. This leaves gaps in understanding how the tool behaves in practice, especially for a data-fetching tool in a tracker system.

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 directly states the tool's function without any fluff. It is front-loaded with the core action ('Get all global fields') and includes essential context ('available in Yandex Tracker that can be used in issues'). Every word earns its place, making it highly concise and well-structured.

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's simplicity (0 parameters, output schema exists), the description is adequate but incomplete. It covers the basic purpose but lacks usage guidelines and behavioral details. The output schema handles return values, so the description doesn't need to explain those. However, for a tool in a tracker system with siblings like 'queue_get_local_fields', more contextual guidance would improve completeness.

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?

The tool has 0 parameters, with 100% schema description coverage. The description does not need to add parameter semantics, as there are no inputs to document. It appropriately focuses on the tool's purpose without unnecessary details, earning a baseline score of 4 for zero-parameter tools.

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 all global fields available in Yandex Tracker that can be used in issues.' It specifies the verb ('Get'), resource ('global fields'), and context ('Yandex Tracker', 'used in issues'). However, it doesn't explicitly differentiate from sibling tools like 'queue_get_local_fields', which handles local fields instead of global ones, leaving some ambiguity about the distinction.

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 mentions the context ('Yandex Tracker', 'used in issues'), but does not specify scenarios, prerequisites, or exclusions. For example, it doesn't clarify if this should be used before creating issues or in conjunction with other tools like 'issue_get' or 'queue_get_local_fields'.

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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