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get_spec_source_info

Retrieve the active spec source and all built-in adapters. Call at session start to let the AI correctly interpret spec semantics from markdown, GitHub, or other sources.

Instructions

Return the active spec source (selected via SPEC_SOURCE env var) plus all adapters built into this server. Call first in any session so the AI knows whether to expect markdown / GitHub / (future) Linear / JIRA / Notion semantics. Returns {active, available, version}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Handler for 'get_spec_source_info' — returns active spec source (SOURCE_NAME env var), available adapters from REGISTRY, and server version.
    def _meta_info(_: dict) -> dict[str, Any]:
        return {
            "active": SOURCE_NAME,
            "available": sorted(REGISTRY),
            "version": __version__,
        }
  • Tool dispatch registration mapping 'get_spec_source_info' to _meta_info handler.
    "get_spec_source_info": _meta_info,
  • Tool schema registration via @app.list_tools() — declares the tool with name 'get_spec_source_info', description, and empty inputSchema (no params).
    Tool(
        name="get_spec_source_info",
        description=(
            "Return the active spec source (selected via SPEC_SOURCE env var) "
            "plus all adapters built into this server. Call first in any "
            "session so the AI knows whether to expect markdown / GitHub / "
            "(future) Linear / JIRA / Notion semantics. "
            "Returns {active, available, version}."
        ),
        inputSchema={"type": "object", "properties": {}},
    ),
  • Imports SOURCE_NAME from config (driven by SPEC_SOURCE env var).
    from .config import SOURCE_NAME
    from .tools import specs as specs_tools
    from .tools import scenarios as scenarios_tools
    from .tools import coverage as coverage_tools
    from .tools import quality as quality_tools
    from .tools import auto_link as auto_link_tools
    from .tools import optimization as optimization_tools
    from .tools import spec_knowledge as spec_knowledge_tools
    from .tools import history as history_tools
    from .tools import telemetry as telemetry_tools
    
    app = Server("mk-spec-master")
    
    
    def _meta_info(_: dict) -> dict[str, Any]:
        return {
            "active": SOURCE_NAME,
            "available": sorted(REGISTRY),
            "version": __version__,
        }
  • Imports REGISTRY from adapters package (populated by adapter registration).
    from .adapters import REGISTRY
    from .config import SOURCE_NAME
    from .tools import specs as specs_tools
    from .tools import scenarios as scenarios_tools
    from .tools import coverage as coverage_tools
    from .tools import quality as quality_tools
    from .tools import auto_link as auto_link_tools
    from .tools import optimization as optimization_tools
    from .tools import spec_knowledge as spec_knowledge_tools
    from .tools import history as history_tools
    from .tools import telemetry as telemetry_tools
Behavior3/5

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

No annotations are present, so the description carries full burden. It discloses the return structure {active, available, version} and that it depends on SPEC_SOURCE env var. However, it does not mention potential failures, rate limits, or behavior if env var is not set.

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?

Two sentences: first states core purpose, second gives usage guidance and return format. No redundant words, front-loaded with critical information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no parameters and no output schema, the description covers the essential: what is returned, when to call, and a brief note on env var. It could elaborate on 'adapters' but is sufficient for a simple informational tool.

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?

Schema coverage is 100% with zero parameters, so baseline is 3. The description adds no parameter details beyond stating no input needed, but it does mention the env var which is not a parameter.

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 uses a specific verb 'Return' and identifies the resource as 'active spec source' and 'all adapters'. It clearly distinguishes from sibling tools by specifying it returns source info and adapters, not spec content or analyses.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly advises 'Call first in any session so the AI knows whether to expect markdown / GitHub / ... semantics', providing clear when-to-use guidance. It does not mention when not to use or alternatives, but the directive is direct and actionable.

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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