jaeger-mcp

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

Annotations already indicate read-only, non-destructive, idempotent, and open-world behavior, but the description adds valuable context: it specifies the underlying API endpoint ('Wraps GET /api/traces/{traceID}'), explains how error spans are identified ('tags["error"] = "true"'), and details the return structure (e.g., per-service statistics, execution tree). This enhances transparency without contradicting annotations.

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 well-structured and front-loaded, starting with a clear purpose statement, followed by API details, error identification, usage examples, and return format. Each section is concise and adds value, with no redundant or wasted sentences, making it efficient for an AI agent to parse.

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 tool's complexity (retrieving detailed trace data), the description is highly complete: it covers purpose, usage guidelines, behavioral context, and return values. With annotations providing safety hints and an output schema detailing the return structure, the description fills all necessary gaps, ensuring the agent has sufficient information for correct tool selection and invocation.

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?

The input schema has 100% description coverage, fully documenting the single parameter (trace_id) with format, length, and example. The description does not add further parameter details beyond what the schema provides, but it reinforces usage context (e.g., 'Obtain from jaeger_search_traces' in schema). Baseline 3 is appropriate as the schema handles 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 clearly states the specific action ('Retrieve full trace detail') and resource ('trace'), distinguishing it from siblings like jaeger_search_traces (which searches) and jaeger_list_services (which lists). It explicitly mentions retrieving 'all spans, service breakdown, and execution tree', making the purpose highly specific.

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 provides explicit guidance with 'Use when' and 'Don't use when' examples, including specific scenarios (e.g., analyzing a slow trace or error) and clear alternatives (e.g., use jaeger_search_traces when lacking a traceID or needing aggregate data). This directly addresses when to use this tool versus its siblings.

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