upload_inline_data

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

All annotation hints are false, so the description carries the full informational burden. It adds valuable behavioral context: the payload-cost implication ('whole payload travels through the model's context'), the text-only constraint with explicit format list, and the architectural distinction that upload_data 'reads the bytes server-side.' It doesn't cover duplicate-name behavior on table creation, but the context it does add goes well beyond the schema.

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?

Four sentences, perfectly front-loaded with purpose, each subsequent sentence earning its place. The second and third sentences work together to define the boundary with upload_data without redundancy. Zero filler words.

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 6-parameter creation tool with an output schema, the description is remarkably complete: it covers use-case boundaries, performance/size implications, supported formats, and fallback paths. The presence of an output schema covers return values, so the description need not explain them. No significant gaps.

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. The description reinforces the data parameter's size sensitivity (hinting why a small inline string is appropriate) and echoes the data_format values (csv/tsv). However, it does not add meaningfully new parameter-level insight the schema descriptions don't already convey, so 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 opens with a specific verb+resource+mechanism: 'Create a small CAS table from inline delimited text passed as a string.' It clearly identifies the resource (CAS table), the input mechanism (inline delimited text), and scopes it as 'small,' which distinguishes it from sibling upload_data. The qualifier 'small' also pre-empts misuse.

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?

Excellent explicit guidance: 'Use this only for tiny, hand-built tables — a lookup/mapping table the model constructs on the fly, or a quick test table — because the whole payload travels through the model's context as a tool argument.' It gives concrete when-to-use examples (lookup tables, test tables), the underlying reason (context size), and explicitly names the alternative tool and why: 'For anything larger, or any file you already have, use upload_data (file_path/url), which reads the bytes server-side instead.'

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