analyze_workflow

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

With no annotations, description carries full burden. It states returns a concise text summary optimized for AI reasoning, implying read-only behavior. Could be more explicit about side effects, but combined with sibling names (analyze_workflow vs modify_workflow), it's sufficiently clear.

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?

Three sentences cover all necessary info: what it does, when to use, and how it differs from get_workflow. No filler, front-loaded with core purpose.

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?

For a tool with 3 params and no output schema, description provides sufficient context: return format, optimization, view options. Slight gap: no mention of error handling or performance for large workflows, but still adequate.

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 coverage is 100%, so baseline is 3. Description adds context beyond schema: explains each view option and recommends using view='list' to see sections before using detail view. This enhances parameter understanding.

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?

Description clearly states it loads a saved workflow and returns structured analysis (sections, node settings, connections, data flow). It distinguishes itself from get_workflow by noting it returns text summary not 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?

Explicitly advises using this tool to understand a workflow before modifying or executing, and directly compares with get_workflow, telling when to prefer which. This provides clear when-to-use and when-not-to-use guidance.

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