execute_query

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

No annotations are present, so the description carries the full burden of behavioral disclosure. It states that a pandas query is executed and that a formatted string is returned, but it does not disclose prerequisites (e.g., that the file must already be loaded/cached), safety implications of executing arbitrary pandas expressions, or failure behavior.

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 concise and front-loaded with the core purpose, followed by Args and Returns sections. Every sentence earns its place, though it is sparse enough that some substance is missing.

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 two-parameter tool with an output schema, the description provides the minimal essentials needed to attempt a call. However, given the large set of sibling tools and the absence of usage guidance or safety context, it is not fully complete for an agent deciding among execute_query, execute_multiline_query, and the pandas-on-timeseries variants.

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 does add meaning by explaining file_hash as the hash of the loaded parquet file and query as the pandas query to execute, but it provides no examples, constraints, or format details beyond the bare schema names and types.

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 states the action ('Execute a pandas query') and the resource ('cached DataFrame'), with file_hash identifying the specific loaded parquet file. It mostly distinguishes itself from siblings like execute_pandas_on_timeseries and execute_pandas_on_html_table by targeting the cached DataFrame, but it does not explicitly distinguish itself from execute_multiline_query.

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 gives no guidance on when to use this tool versus alternatives. It does not mention that this is for single-line pandas queries, nor does it point to siblings such as execute_multiline_query or execute_pandas_on_timeseries for other cases.

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