spss_read_data

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

No annotations exist, so the description carries the full behavioral burden. It discloses genuinely useful traits: output as a Markdown table, optional variable filtering, row-count limiting, and no SPSS installation requirement. However, it does not mention value-label behavior even though apply_value_labels defaults to true, and it never explicitly confirms the read-only safety profile.

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 short sentences with the core function front-loaded. Every sentence adds information — no filler, no repetition of schema defaults. Appropriately sized for the tool's low-to-moderate complexity.

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 low-complexity read tool the description covers the essentials, and the presence of an output schema handles return-value documentation. But with no annotations and 0% schema coverage, the undocumented apply_value_labels parameter and the lack of routing guidance against spss_read_metadata leave meaningful gaps. Adequate, but an agent could still misfire on value-label expectations.

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 description coverage is 0%, so the description must compensate. It translates two parameters into plain language ('filter to specific variables' → variables, 'limit row count' → max_rows) and implies file_path is the .sav path. However, apply_value_labels is documented nowhere in either the schema or description, leaving one of four parameters completely unexplained.

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?

States a specific action with resource and output format: 'Read rows of data from an SPSS .sav file as a Markdown table.' The 'rows of data' phrasing clearly distinguishes it from the close sibling spss_read_metadata, which reads metadata rather than data. An agent can tell this is the data-reading tool among many analysis siblings without opening schemas.

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 implies when to use it — whenever actual data rows from a .sav file are needed — but never contrasts it with alternatives like spss_read_metadata, spss_list_variables, or spss_file_summary. The 'Does not require SPSS to be installed' note is useful context but not usage routing. No explicit when-not-to-use guidance is present.

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