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parsiya

Trailmark MCP Server

by parsiya

callees_of

Find all functions directly called by a given node in a code graph, enabling dependency analysis.

Instructions

Return direct callees of a node.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
session_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • MCP tool handler registration for 'callees_of'. This is the @mcp.tool()-decorated function that exposes the tool to the MCP server. It delegates to app_runtime.callees_of().
    @mcp.tool()
    def callees_of(name: str, session_id: str | None = None) -> list[dict[str, Any]]:
        """Return direct callees of a node."""
        return app_runtime.callees_of(name, session_id=session_id)
  • TrailmarkRuntime.callees_of() service layer. Delegates to the QueryEngine's callees_of method via the scanned engine handle.
    def callees_of(self, name: str, session_id: str | None = None) -> list[dict[str, Any]]:
        return self._require_scanned_handle(session_id).engine.callees_of(name)
  • ToolSpec registration for 'callees_of' in the tool catalog with schema definition (name parameter required, session_id optional).
    ToolSpec(
        name="callees_of",
        category="navigation",
        description="Return direct callees of the source node.",
        parameters={"name": _param("string", required=True), "session_id": SESSION_ID_PARAM},
    ),
Behavior2/5

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

No annotations are present, so the description carries full burden. It only states the basic function without disclosing edge cases, side effects, or requirements such as node existence or performance implications.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very short (one sentence), but it lacks critical details like parameter explanations. While concise, it is under-specified.

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?

Given the tool returns graph data and has two undocumented parameters, the description is incomplete. It does not cover input requirements, output format, or edge cases.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description adds no meaning beyond parameter names. It does not explain what 'name' or 'session_id' represent, leaving the agent to infer.

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 tool returns direct callees of a node, using a specific verb and resource. It distinguishes from sibling 'callers_of' by specifying direction.

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?

No guidance is provided on when to use this tool versus alternatives like 'paths_between' or 'subgraph'. The description lacks context for selecting this tool.

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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