Margin of Insight — Equity Research

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

With no annotations provided, the description carries the weight of behavioral disclosure. It explicitly states what the tool returns ('business overview, financial snapshot, and recent catalysts') and that no payment is required. This adds meaningful context about the operation and its accessibility, though it does not cover rate limits, data freshness, or any potential side effects. It is transparent enough for a read-oriented tool.

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. It front-loads the purpose and then details the return content and a key differentiator ('No payment required'). Every word earns its place, with no redundancy or tangential information.

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 is simple (one parameter, no output schema), and the description provides adequate context by stating what the primer includes. It does not explain the exact format or structure of the return, but the three components listed (overview, financial snapshot, catalysts) give a clear sense of the output. It is complete enough for a straightforward read-only tool.

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 provides complete coverage for the single parameter 'ticker', including a descriptive example ('AAPL, MSFT, NVDA'). The description does not add further parameter details, but given the high schema coverage, the baseline of 3 applies. The description adds no extra meaning beyond what the schema already conveys.

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: 'Get a free company primer for any US-listed company.' The verb 'Get' it is unambiguous and the resource is defined. The scope is specific ('US-listed company'), and it distinguishes itself from siblings like 'get_fundamental_analysis' and 'get_investment_memo' by focusing on a primer (overview) rather than deep analysis.

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—it is for obtaining a quick company primer with basic business and financial info. It does not explicitly state when to use this tool instead of siblings such as get_fundamental_analysis or get_investment_memo, nor does it mention any exclusions. The 'free' and 'primer' wording hints at a lightweight overview, but clear guidance is missing.

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 full behavioral burden. It transparently discloses the $1.00 cost, the required Stripe Shared Payment Token, and the Authorization header format, which are critical operational details beyond the schema. It also lists the 12 research steps, providing clarity on the processing scope.

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 tool's purpose, followed by a compact list of research steps and then the cost/payment details. Every sentence contributes essential information with no redundancy, making it concise and well-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?

Given the simple single-parameter input and absence of output schema, the description covers the essential aspects: purpose, research content, cost, and payment mechanism. It could benefit from stating expected return format, but the tool's name and step list make the output obvious enough for most use cases.

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 the single 'ticker' parameter with a clear description. The tool description adds no additional parameter semantics, so it does not go beyond the schema's baseline coverage.

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 retrieves fundamental analysis and enumerates the specific research steps covered, giving a precise scope. However, it does not explicitly contrast with sibling tools like get_company_primer or get_investment_memo, so it lacks direct differentiation.

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 prefer this tool over alternatives such as get_company_primer or search_research. It only states what the tool does and its cost, leaving 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?

With no annotations, the description carries the burden and does well: it explicitly discloses $2 cost, the need for a Stripe Shared Payment Token, and the exact Authorization header format. It doesn't cover error handling or response format, but the primary side effect (payment) is 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?

Three dense sentences: purpose and contents, cost, and payment mechanism. Every sentence earns its place and the critical information is front-loaded. No repetition or fluff.

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 paid research tool, it covers what the memo contains, the cost, and how to pay. No output schema exists, so return format is not explained, and there is no sibling guidance, but the core operational context is sufficiently complete.

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% and the only parameter 'ticker' is described as 'Stock ticker symbol'. The description adds no extra parameter-level details (e.g., formatting, case sensitivity), so with high schema coverage the 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?

Description opens with 'Get a full investment memo', a specific verb+resource, and enumerates detailed content (DCF valuation, moat analysis, etc.) that clearly separates it from sibling research tools. The scope and nature ('Equivalent to a sell-side initiation note') further sharpen the purpose.

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 does not explicitly name alternatives, but 'Equivalent to a sell-side initiation note' gives context for when a deep full-memo is appropriate. Cost and payment instructions are practical usage guidance, though it lacks a direct 'use this instead of X' comparison.

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 for behavioral disclosure. It reveals that the tool is free and lists data, but it does not state whether it is read-only, whether there are rate limits, pagination, or if any authentication is required. 'Free' is the only extra behavioral trait, which is insufficient for full transparency.

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 that front-loads the primary action and details. Every word earns its place, and there is no redundant or vague 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?

For a simple list tool with no parameters and no output schema, the description is largely complete: it names the tool's output fields and a key characteristic (free). However, it does not explicitly state the output format (e.g., array of objects) or any sorting/pagination, which would make it fully self-contained. Given the low complexity, this is an adequate score.

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?

With zero parameters, the schema is empty and the baseline is 4. The description adds value by specifying the output fields, which clarifies what the empty call returns. Although no parameter details are needed, the description does not contradict or mislead about parameters.

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 a specific verb ('List') with a clear resource ('all covered tickers') and enumerates the exact fields returned (company name, sector, exchange, last updated date). This clearly distinguishes it from the sibling tools which focus on detailed research, primers, or requests for coverage.

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 the tool is used to see which tickers are covered, but it does not explicitly state when to use it versus alternatives like search_research or request_coverage. There is no mention of exclusions or context. The 'Free' hint weakly suggests cost/capability context, but not enough to be considered clear 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?

No annotations are present, so the description carries the full burden. It usefully discloses that coverage is typically added within 24-48 hours and that the service is free, but it does not describe what happens after submission, whether duplicate requests are allowed, or how invalid tickers are handled.

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 short, focused sentences capture the action, eligibility, timeline, and cost. No filler or redundant content; 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 simple 2-parameter tool with no output schema or annotations, the description covers the core requirements: what the tool does, who it is for, what the expected turnaround is, and that it is free. Minor gaps remain around the exact response or next steps after a successful request.

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 the baseline is 3. The description adds extra meaning by constraining the ticker to US-listed companies not yet in the coverage universe, which goes beyond the schema's generic 'Stock ticker symbol to request coverage for' description.

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 ('Request research coverage') and the target resource ('US-listed company not yet in the coverage universe'). The verb is specific and distinguishes this tool from sibling retrieval tools like list_coverage and search_research.

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 phrase 'not yet in the coverage universe' provides clear context for when to use this tool. However, it does not explicitly mention alternatives or what to do for companies already covered, only implicitly excludes them.

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 burden. It states the return value (matching companies with ticker, name, sector, exchange) and adds 'Free', but does not disclose whether the operation is read-only, case-sensitivity, or handling of no matches. Basic transparency exists, but deeper behavioral traits are not disclosed.

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 sentences, front-loaded with the primary action and return fields. Every word adds value, including the note about being 'Free'. No redundancy.

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 single-parameter search tool with no output schema, the description covers what it does, how to call it, and what to expect. It doesn't mention behavior on empty results or how it relates to the sibling coverage tools, but for its complexity, it is nearly complete.

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 covers 100% of the parameter with a description ('Ticker symbol or company name to search for'). The tool description reiterates the same, adding no new meaning beyond the schema. Baseline 3 applies since schema coverage is complete.

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 verb ('Search') and resource ('covered companies') with explicit parameters (ticker or company name). It clearly distinguishes from siblings like get_company_primer or list_coverage by focusing on discovery rather than deep analysis or full listing.

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: search for a company to get basic identifiers. However, it does not explicitly state when to use this tool versus siblings, or any exclusions (e.g., 'use list_coverage for a full list'). No direct alternative or context is given, so the usage is implied but not explicit.

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