read_text

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

Discloses backend-dependent behavior (some backends expose aggregate descendant text). No annotations provided, but description misses error cases or side effects. Output schema exists, so return format is covered elsewhere.

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?

Extremely concise: first line states purpose, followed by two sentences on backend behavior and workflow. No redundant words, and information is front-loaded.

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 simple nature (one parameter) and existing output schema, the description covers workflow, backend variability, and parameter usage. Could mention error handling, but overall 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?

Single parameter 'element_id' has no schema description (0% coverage), but the tool description explains it fully: 'The element or container ID to read text from.' This adds essential meaning beyond the schema.

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?

Clearly defines the tool's function: returning text content from elements or containers. Specifies verb 'read' and resource 'text', and distinguishes from sibling tools like snapshot and select_text by focusing on reading text content.

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?

Provides explicit usage workflow: snapshot() then read_text(). Explains when to use with containers ('Pass a container ID from snapshot()'). Does not explicitly state when not to use or list alternatives, but context is clear.

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