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Yandex Tracker MCP

get_issue_types

Retrieve all available issue types from Yandex Tracker to streamline creating or updating tasks effectively.

Instructions

Get all issue types available in Yandex Tracker that can be used when creating or updating issues

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler function decorated with @mcp.tool for the 'get_issue_types' tool. It retrieves issue types by calling the fields protocol method.
        description="Get all issue types available in Yandex Tracker that can be used when creating or updating issues"
    )
    async def get_issue_types(
        ctx: Context[Any, AppContext],
    ) -> list[IssueType]:
        issue_types = await ctx.request_context.lifespan_context.fields.get_issue_types(
            auth=get_yandex_auth(ctx),
        )
        return issue_types
  • Registers all tools, including 'get_issue_types', to the FastMCP server instance by calling register_tools.
    register_tools(settings, mcp)
  • Core implementation in TrackerClient that performs the HTTP GET request to Yandex Tracker's /v3/issuetypes endpoint to fetch issue types.
    async def get_issue_types(
        self, *, auth: YandexAuth | None = None
    ) -> list[IssueType]:
        async with self._session.get(
            "v3/issuetypes", headers=await self._build_headers(auth)
        ) as response:
            response.raise_for_status()
            return IssueTypeList.model_validate_json(await response.read()).root
  • Caching wrapper around the get_issue_types method if tool caching is enabled.
    async def get_issue_types(
        self, *, auth: YandexAuth | None = None
    ) -> list[IssueType]:
        return await self._original.get_issue_types(auth=auth)
  • Protocol defining the interface for global data methods including get_issue_types, providing type definitions for inputs and outputs.
    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. It states the tool retrieves data ('Get'), but lacks details on behavioral traits such as rate limits, authentication needs, pagination, or response format. The mention of 'available in Yandex Tracker' adds minimal context, but overall, it's insufficient for a mutation-free tool with no annotation coverage.

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 purpose and usage context without any wasted words. It's front-loaded and appropriately sized for its function.

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 0 parameters, 100% schema coverage, and an output schema exists, the description doesn't need to explain return values. However, with no annotations and a read operation, it should ideally mention more about behavioral aspects like data scope or limitations. It's adequate but has gaps in transparency.

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, and schema description coverage is 100%, so there's no need for parameter details in the description. The description doesn't add or contradict parameter information, which is appropriate, 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 with a specific verb ('Get') and resource ('issue types'), and specifies the context ('available in Yandex Tracker'). It doesn't explicitly differentiate from siblings like 'get_priorities' or 'get_statuses', which is why it's not a 5.

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?

The description implies usage by mentioning 'when creating or updating issues', providing some context. However, it doesn't explicitly state when to use this tool versus alternatives like 'get_global_fields' or 'queue_get_local_fields', nor does it mention exclusions or prerequisites.

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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