analyze_workflow

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

No annotations present, so description carries full burden. It discloses that the tool returns a text summary (not raw JSON) optimized for AI reasoning and that it loads a saved workflow. It does not explicitly state non-destructiveness but implies read-only analysis. Minor gap in stating side effects or permissions, but overall transparent.

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 three sentences, front-loaded with purpose and key details. No unnecessary words; every sentence adds value. Highly concise and well-structured.

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 no output schema, 3 parameters, and no annotations, the description adequately explains the tool's function, output format (text summary), and use case. Could elaborate on return structure, but the summary is sufficient for reasonable understanding. Minor gap for completeness.

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 has 100% coverage with detailed parameter descriptions (e.g., view enum meanings, filename usage). The tool description adds high-level purpose but does not add meaning beyond the schema. Baseline 3 is appropriate.

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 specifies the verb 'load' and 'return a structured analysis', identifies the resource 'workflow', and details output components (sections, node settings, connections, data flow). It explicitly distinguishes from sibling tool get_workflow, which returns raw JSON.

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 explicitly states when to use this tool ('understand any workflow before modifying or executing it') and when not to ('unless you need the raw JSON for enqueue_workflow or modify_workflow'), providing clear guidance against alternatives.

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