dca-tools

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

With no annotations, the description carries the burden. It discloses the return content (explanation and links), which is sufficient for a simple read-only tool. No hidden side effects or auth needs are apparent, so it is adequately transparent.

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?

Single sentence that immediately states the tool's purpose and what it returns. No extraneous words, well front-loaded. Every word earns its place.

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?

Given no parameters and no output schema, the description is mostly complete—it specifies the content of the response. However, omitting details like format or how the links are structured leaves minor ambiguity for an agent, but not critical.

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?

There are zero parameters, so baseline score is 4. The description adds no parameter info, but none is needed since schema coverage is 100% with no params.

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 returns a concise explanation of dollar-cost-averaging plus links to methodology, calculators, and blog. It distinguishes itself from siblings like list_assets and run_dca_backtest by focusing on static documentation retrieval.

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 when needing a DCA summary, but does not explicitly specify when not to use it or mention alternatives. No exclusions or context provided.

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 adequately conveys the basic behavior (listing assets) but lacks disclosure of any side effects, rate limits, or other behavioral traits beyond the listed functionality.

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 the main purpose, and adds the filter detail without any wasted 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 simple list tool with one optional parameter, the description is nearly complete. It mentions the return fields (category, URL, historical data) but could note that it is read-only and has no side effects.

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 schema coverage is 100% and the description essentially repeats the parameter info from the schema, adding no new meaning beyond what the optional category filter and its enum values provide.

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 returns assets with specific fields (category, URL, historical data availability), distinguishing it from sibling tools like run_dca_backtest which performs backtests.

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 does not provide explicit when-to-use guidance or differentiate from alternatives. It only states what it does, leaving it to the agent to infer usage context.

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 must carry the full burden. It mentions that dates outside the available range are clamped, which is a useful behavioral trait. However, it does not disclose whether the operation is read-only, what authentication is required, or any rate limits. For a simulation tool, the behavioral disclosure is adequate but not exhaustive.

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 brief (3 sentences), well-structured, and front-loaded with the core action. Every sentence adds value: what it does, what it returns, and a key behavioral note. No unnecessary 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?

The description lists the return fields (total invested, final value, profit, CAGR, etc.), which is helpful given no output schema. It covers the main behavior and caveat (date clamping). However, it does not mention error states or edge cases beyond date clamping, which could be improved.

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 adds context by mentioning date clamping behavior for start_date and end_date, but otherwise does not add significant meaning beyond the schema descriptions. The description as a whole gives context but not parameter-level details.

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?

Description clearly states it simulates dollar-cost averaging into one asset using a specific engine. The verb 'simulates' and the resource 'backtest' align with the tool name. It distinguishes from siblings (get_method, list_assets) which are about retrieving methods or asset lists, not backtesting.

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 clearly explains the tool's purpose and what it returns, but does not explicitly state when to use it versus alternatives. Given the siblings, it's implied this is the backtesting tool, but no explicit 'when not to use' guidance is provided.

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