datacore_date

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

No annotations are provided, so the description carries the full burden. It transparently states the tool's behaviors (returns today's date, validates day-of-week, adds/subtracts days, etc.) and explains the underlying problem it solves (LLM hallucination). While it doesn't explicitly mention side effects or read-only nature, the tool is inherently a pure calculation function, making the description sufficient.

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 two sentences, front-loaded with an imperative rule and rationale, followed by a compact enumeration of capabilities. Every word earns its place, with no redundancy or unnecessary details.

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 simple date utility with one required parameter and fully documented schema, the description is largely complete. It does not specify return value formats (e.g., exact output of diff or parse), but the schema's op enumeration provides sufficient context. Since there is no output schema, slightly more detail on outputs would push this to a 5, but the low complexity keeps it 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 description coverage is 100%, so the baseline is 3. The description enumerates operation categories but does not add parameter-level semantics beyond the schema. It does provide context on why certain operations exist (e.g., validation to counter hallucination), but this is not parameter-specific detail.

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 identifies the tool as a canonical date operations utility and enumerates all supported operations (today, dow, validate, add/sub, diff, parse, org-stamp). It distinguishes itself from sibling tools (datacore_capture, datacore_search, datacore_ingest, datacore_status) by being the only date-related utility.

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 instructs 'ALWAYS use this instead of typing dates from memory', providing a clear when-to-use directive with the rationale that LLMs hallucinate day-of-week names and training-era dates. This effectively tells the agent to prefer this tool for any date calculation, overriding memory-based guesses.

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