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

With no annotations provided, the description carries the full burden of behavioral disclosure. It implies a write operation ('capture', 'record') but does not explain side effects, persistence, permissions, or return values. The agent is left guessing what happens after the capture and whether it has any irreversible effects.

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 concise sentences, front-loaded with the core purpose and followed by usage guidance. Every word adds value; there is no fluff or repetition of schema 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 tool with 4 parameters and no output schema, the description covers the 'what' and 'when' but not the 'how' behaviorally. It doesn't mention what happens after capture (e.g., storage, retrieval via search), error conditions, or idempotency. It's adequate but leaves gaps in understanding the full workflow.

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 75%, so the baseline is 3. The description adds little beyond the schema, only reinforcing the type distinction via 'journal entry or knowledge note'. It doesn't explain nuanced parameter usage (e.g., when title is required, how tags are used) beyond what the schema already provides.

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 tool captures a journal entry or knowledge note, giving a specific verb and resource. It also provides examples of when to use it (decisions, meetings, events), which helps differentiate from sibling tools like search and status, though it doesn't explicitly contrast with datacore_ingest.

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 clear proactive usage context: 'Call proactively to record important decisions, meeting outcomes, and significant events.' It does not explicitly state when not to use it or mention alternatives, but the guidance is sufficient for typical use cases.

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

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.

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

With no annotations, the description carries the full burden. It only states the action ('ingest') but does not disclose side effects, permissions, or what happens to the content once ingested. For a write operation, this lacks necessary behavioral detail.

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 a single succinct sentence with no filler words. It is appropriately sized for the tool's apparent simplicity and communicates the core purpose efficiently.

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?

The tool has no annotations, an output schema, and minimal schema coverage. The description omits information about how the content is stored, the role of tags and title, and any return values or side effects. Given the lack of annotations and output schema, this is incomplete for an agent to fully understand the tool's behavior.

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 only 33%, with only 'content' described. The description mentions 'text content' but does not explain the 'tags' or 'title' parameters, nor does it compensate for the missing schema descriptions. It adds little beyond the parameter names.

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 tool ingests text content as a knowledge note, using a specific verb and resource. However, it does not explicitly distinguish it from sibling tools like datacore_capture, which may perform a similar function.

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?

No guidance is provided on when to use this tool versus alternatives like datacore_capture. The description simply states the action without any contextual or exclusionary information.

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

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

No annotations are provided, so the description carries full responsibility for behavioral disclosure. It only states the action 'Search', which implies read-only, but does not explicitly confirm non-destructiveness, explain result format, or mention any potential side effects (e.g., semantic search may have performance or requirement implications). The description adds minimal behavioral context beyond the core action.

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 a single, front-loaded sentence that is extremely concise and to the point. Every word contributes meaning, and there is no redundant or verbose language.

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?

With 4 parameters, no output schema, and no annotations, the description is insufficiently complete. It does not mention the available search methods (keyword vs semantic), the scope filtering, or the limit parameter. It also does not describe what the search results will contain or any pagination/sorting behavior. This leaves the agent under-informed for selecting and invoking the tool with appropriate arguments.

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 75%, so the schema already documents query, limit, and method. The description adds 'journal entries and knowledge notes' which aligns with the scope enum values, but does not explain the scope parameter or provide meaningful enrichment. It largely repeats what the schema describes, so it meets the baseline but does not compensate for the remaining 25% gap (scope).

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 tool's function: 'Search journal entries and knowledge notes by keyword'. The verb 'search' is specific, the resources are named (journal entries and knowledge notes), and it differentiates from sibling tools like datacore_capture or datacore_ingest which perform different actions.

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 usage for searching, but does not explicitly state when to use this tool over alternatives or provide exclusions. While it is obvious from the name and description that this is the search tool among the siblings, there is no guidance on when to choose keyword vs semantic search or which scope to use. The mention of 'by keyword' could mislead users into thinking semantic search is not supported.

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

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

With no annotations provided, the description carries the full burden of disclosing behavior. It states the information returned (note counts, module health, update info), which is helpful, but it does not explicitly confirm that the operation is read-only or mention any potential side effects, latency, or authentication requirements.

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 a single, front-loaded sentence that conveys the tool's purpose without any redundant information. Every word contributes to understanding what the tool does.

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, zero-parameter status tool with no output schema, the description adequately covers the key aspects: it states the resource (Datacore) and the specific status categories (note counts, module health, update info). While it could mention whether the status is real-time or cached, this is a minor gap and the description is sufficiently complete for its complexity.

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?

The tool has zero parameters, and the input schema is empty, so schema description coverage is trivially 100%. The description does not need to elaborate on parameter semantics, and the baseline for zero-parameter tools is 4, which is appropriate here.

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 uses the specific verb 'Show' and resource 'Datacore status', explicitly listing what users can expect: note counts, module health, and update info. This clearly distinguishes the tool from sibling tools like datacore_capture, datacore_search, datacore_ingest, and datacore_date, which perform different operations.

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 this tool is used for checking overall Datacore system health, but it does not explicitly state when to use it versus alternatives or when not to use it. With no competing status tools among siblings, the context is clear but the guidance is not explicit.

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