trace-mcp

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 mentions the tool analyzes parameter flow, mutations, and returns, but doesn't disclose behavioral traits like whether it's read-only, performance characteristics, rate limits, or what the output format looks like. For a complex analysis tool with zero annotation coverage, this is a significant gap in transparency.

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

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

The description is efficiently structured in two sentences: the first explains the core analysis purpose with specific details, and the second clarifies the scope limitation. Every word earns its place with zero waste, making it easy to parse and understand quickly.

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

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

Given the complexity of dataflow analysis, no annotations, and no output schema, the description is incomplete. It doesn't explain what the output looks like (e.g., a graph, report, or structured data), performance implications, error conditions, or how it differs from similar tools like taint_analysis. This leaves significant gaps for an AI agent to use it effectively.

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

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

Schema description coverage is 100%, so the schema already documents all four parameters (symbol_id, fqn, direction, depth) with clear descriptions. The description adds no additional parameter semantics beyond what's in the schema, such as explaining trade-offs between direction options or depth limits. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool performs 'intra-function dataflow analysis' with specific details: tracking parameter flow through the function body, into calls, mutations, and returns. It clearly distinguishes this from sibling tools like get_call_graph or get_control_flow by focusing on parameter-level data flow within a single function scope (Phase 1).

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

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

The description implies usage for analyzing parameter flow within a single function ('Phase 1: single function scope'), suggesting it's for detailed intra-function analysis rather than broader system analysis. However, it doesn't explicitly state when to use this versus alternatives like taint_analysis or get_control_flow, nor does it mention prerequisites or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.