Equity Rank

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

Annotations already declare readOnlyHint=true, and the description adds that the data is public, figures are model estimates, and it is not advice, plus a citation URL. This gives an agent useful behavioral context beyond the schema and annotations with no contradiction.

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 front-loaded, compact, and dense with useful information: it lists the return fields in one sentence and adds a disclaimer in the next. There is no filler 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?

With one simple required parameter, no output schema, and read-only annotations, the description covers invocation, return contents, source URL, and the model-estimate limitation. An agent has enough context to call the tool correctly and interpret its output.

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 input schema already fully documents `id` as a ticker symbol with an example. The description adds that the id is returned by `search`, which is helpful but only marginal additional semantic value beyond what the schema 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 it retrieves Equity Rank's public valuation summary for one security by ticker, and it enumerates the specific fields returned. It distinguishes itself from `search` and `list_sector_stocks` by framing the id as a ticker returned by `search`, but it does not explicitly differentiate itself from the similarly named `get_stock_valuation` sibling.

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 provides a clear use context: call this when you need a single security's valuation summary, and obtain the id from `search`. It does not explicitly state when not to use it or mention alternatives such as `get_stock_valuation`, so it stops short of full routing guidance.

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?

Annotations already signal read-only and closed-world behavior. The description adds valuable behavioral detail: uncovered tickers return an error, the data is model estimates only and not advice, and the payload is explicitly matched to a public API endpoint. This goes beyond the annotations without contradicting them.

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 front-loaded with the core purpose, then details the return payload, then states error and disclaimer caveats. Each sentence earns its place, though the long field enumeration is dense and could be slightly better structured.

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 no output schema, the description effectively compensates by listing all returned fields, the error condition for uncovered tickers, and the advisory caveat. For a single-parameter read-only tool, this provides everything an agent needs to call it correctly and interpret the result.

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 input schema covers 100% of the parameter meaning with a clear type and example ('NVDA'). The description adds little beyond reaffirming 'one ticker', so the baseline of 3 is appropriate: the schema carries the parameter documentation burden.

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 names a specific verb ('get') and resource ('a stock's model valuation'), and defines the scope as 'one ticker'. It enumerates the exact fields returned, making the tool's function unambiguous and clearly distinct from sibling tools like list_sector_stocks or search.

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 implies the tool is for retrieving valuation data for a single ticker, and the error condition for uncovered tickers adds useful context. However, it does not explicitly state when to prefer this tool over siblings, nor does it provide any when-not-to-use guidance or alternative routing.

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?

Annotations already provide readOnlyHint=true, and the description adds useful behavioral context: only public-safe fields are returned, no fair value per row, and results are ranked by Equity Rank Score. This goes beyond the schema and annotations without contradicting them.

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 sentences, each earning its place: the first defines the output, the second enumerates valid sector values, and the third clarifies scope and points to the alternative. The most important information is front-loaded, with no filler.

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?

There is no output schema, so the description must explain the return shape; it lists all key fields per row. It also covers sector value flexibility, the public-safe limitation, and the fair-value alternative, making it complete enough for an agent to call this tool correctly without additional context.

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% for both limit and sector, so the baseline is 3. The description adds meaningful value by enumerating the accepted sectors and noting that finer labels like 'Banks' or 'Software' also match, which helps the agent construct valid sector values beyond the schema's single example.

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 names a specific action (list) and resource (a sector's scored stocks), and details exactly what each row contains: Equity Rank Score, margin-of-safety differential, P/E, market cap, and dividend yield. It also distinguishes itself from get_stock_valuation by explicitly stating fair value is not included, making the tool's purpose unambiguous.

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?

It clearly states when this tool is appropriate (public sector index, scored stocks) and explicitly routes fair-value needs to get_stock_valuation. It does not contrast with search or fetch, but the context is strong enough that an agent can infer when to use it over those siblings.

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?

The annotations already declare readOnlyHint=true, so the safety profile is covered. The description adds useful behavioral detail: returns at most 10 matches, includes the id for `fetch`, and provides the public page URL. This goes beyond the annotations without contradicting them.

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?

Two targeted sentences with no filler. The first sentence states what the tool does, the second covers output and usage guidance. Every part earns its place and the most important information is front-loaded.

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 single-parameter read-only search tool with full schema coverage and annotations, the description is complete. It explains what is searched, the result limit, the key output fields, and how to proceed to `fetch`. Nothing essential is missing.

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%: the `query` parameter is fully documented with an example. The description only restates that the query can be a ticker or company name, so it does not add meaningful semantics beyond the schema. Baseline of 3 is appropriate.

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 states a specific action and target resource: 'Find securities in Equity Rank's coverage universe by ticker or company name.' It also distinguishes the tool by noting it returns the id needed for `fetch` and the public page URL to cite, making its role clear relative to siblings.

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 explicit guidance: 'Use this first when you only have a company name.' This clearly tells the agent when to reach for this tool, though it does not explicitly discuss when not to use it or compare it to get_stock_valuation/list_sector_stocks.

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