Stock Bloc

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 mentions the return of structured intelligence and a price, but does not state whether the tool is read-only, has side effects, requires authentication, or how it behaves on errors. This is a significant gap for a tool that likely makes external API calls.

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 sentence that front-loads the core action and form types, then appends the agent name and price. It is concise with no redundant wording, though the inclusion of the agent name and price adds minor clutter. Overall it earns a 4 for efficiency.

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 no output schema and no annotations, the description is insufficiently complete. It does not explain what 'structured financial intelligence' means, what fields are returned, or how errors are handled. Given the tool's complexity (three parameters, no output schema), an agent would need more detail to invoke it reliably.

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 schema already documents ticker, question, and filingType with descriptions. The description adds no extra meaning beyond the schema; it merely references the forms which are already enumerated. According to the baseline, a score of 3 is appropriate when the schema does the heavy lifting.

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 analyzes SEC filings (10-K, 10-Q, 8-K) and returns structured financial intelligence. It names a specific verb and resource, and the forms distinguish it from siblings like search_13f_whale_filings. However, it doesn't explicitly contrast with other analysis tools, so it falls short of a 5.

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 SEC filings by listing the form types, but provides no explicit guidance on when to use this tool versus alternatives such as search_13f_whale_filings. There are no 'when not to use' or 'use instead' statements, leaving the agent to infer context from sibling names.

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 behavioral disclosureтное. It states the tool will run analysis but does not explain data sources, latency, whether it fetches live data, what 'AI analysis' means concretely, or any limits. For a tool that produces a report-like result, this is a meaningful gap.

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 with no filler. It efficiently conveys the tool's scope, though the trio 'AI market analysis, fundamental metrics, and technical signals' is slightly listy and could be tightened.

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 one parameter and no output schema, so the description should clarify what the caller receives. It gives high-level output categories but no indication of return format, report structure, or how to interpret the AI analysis. For a tool named analyze_stock_ai, this is under-specified.

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, symbol, is already documented with examples. The description reinforces that any ticker is accepted and adds that fundamental metrics and technical signals will be analyzed, but it adds no new format or value beyond the schema's baseline.

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 clear verb and resource: run comprehensive AI market analysis, fundamental metrics, and technical signals for a stock ticker. It distinguishes itself from get_stock_quote by promising broader analysis, though it does not explicitly name sibling tools to contrast against.

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 should be used when a stock ticker's broader market analysis is needed, but it gives no explicit when-to-use guidance, prerequisites, or exclusions. It does not address how this differs from run_quant_simulation, evaluate_tsunami_strategy, or get_top_trade_ideas, leaving selection largely to inference.

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. It reveals the output metrics, which is useful, but it does not disclose whether the operation is read-only, whether it has side effects, how the backtest is performed, or what assumptions it makes. This is minimal behavioral context for a tool with no annotation coverage.

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, well-structured sentence that front-loads the action, defines the evaluation universe with the ticker list, and ends with the output metrics. Every element serves a purpose with no filler or 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?

The description covers the purpose and return metrics, and the schema fully documents parameter values, giving an agent a reasonable starting point. However, with no output schema and no annotations, the tool would benefit from mentioning defaults (benchmark, horizon), allocation constraints, and metric interpretation. These are moderate gaps for a 4-parameter tool with a nested input.

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 schema already documents all four parameters. The description adds context about the watchlist and output metrics, which helps interpret the allocation parameter, but it does not materially expand on parameter meanings beyond what the schema provides. The baseline 3 applies.

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 ('Evaluate') and a specific resource ('quantitative portfolio strategy against the Super Sonic Tsunami infrastructure watchlist') with the ticker list, and it names the output metrics (Alpha, Sharpe, Win Rate, Drawdown). This makes the tool's role clear, though it does not explicitly distinguish it from the sibling 'run_quant_simulation', which may also evaluate strategies.

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 explicit guidance on when to use this tool versus alternatives like run_quant_simulation, and it does not mention exclusions or prerequisites. The only implicit signal is the watchlist keyword, which hints at the intended scenario but does not constitute real usage 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool fetches ranked agents and lists the data points returned, which is useful, but it doesn't disclose whether this is a read-only operation, whether it requires authentication, or whether there are any rate limits or caching behaviors. The description is accurate but lacks depth on behavioral traits.

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, concise sentence that front-loads the core purpose and lists the key data points. It is efficient and free of filler, though it could be slightly more structured by separating the purpose from the return 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 read-only list tool with one optional parameter and no output schema, the description is mostly complete. It tells the agent what data will be returned, but it doesn't mention pagination, default behavior beyond the schema's 'default: 10', or any authentication requirements. Given the tool's simplicity, this is adequate but not exceptional.

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 schema already documents the 'limit' parameter. The description adds context about the ranking ('top ranked') and the data points returned, but it doesn't add specific parameter semantics beyond what the schema provides. Baseline 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 clearly states the tool fetches top ranked Stock Bloc AI agents and lists specific data points (win rates, alpha returns, badges, trade recommendations). It uses a specific verb ('Fetch') and resource ('top ranked Stock Bloc AI agents'), which distinguishes it from siblings like get_stock_quote or get_top_trade_ideas, though it doesn't explicitly name a sibling alternative.

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 context by describing what data the tool returns, but it doesn't explicitly state when to use this tool versus alternatives like get_top_trade_ideas or get_stock_quote. There is no mention of exclusions or conditions that would route an agent to a different tool.

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 burden of behavioral disclosure. 'Check' implies a read-only operation, and the description names the outputs (timestamps and stale flags), but it does not disclose response format, potential errors, rate limits, or any side effects. This is adequate for a simple status read but not rich.

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 dense sentence with no filler. The primary action and scope are front-loaded, and every phrase earns its place by specifying what is checked and across which feeds.

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 zero-parameter, no-output-schema status tool, the description is largely complete. It defines what the tool checks and the scope. It could be more complete by briefly noting the output shape or that no arguments are required, but the low complexity makes this a minor gap.

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, so the baseline is 4. There is nothing to document, and the description adds useful context about the data categories covered (market, 13F, and intelligence feeds). No parameter semantics are missing.

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 ('Check') and a clear resource: pipeline data freshness, updated_at timestamps, and stale boolean flags across market, 13F, and intelligence feeds. This clearly differentiates it from sibling tools like get_stock_quote or analyze_sec_filing, which are about market data or filings rather than internal pipeline status.

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 for checking data freshness and staleness status, but it does not explicitly state when to use it versus alternatives or when not to use it. There are no exclusions or alternative routing clues, so the usage guidance is only implicit.

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 states the operation is a read (get) and the output includes PDF links, which is some transparency. However, it does not disclose what happens if ebookId is omitted, whether it returns a list vs single item, or any potential errors/permissions. For a simple read tool, this is adequate but not rich.

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 with no filler. It immediately states the purpose and the deliverable (info + PDF links). Every word earns its place; it's 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?

For a tool with one optional parameter and no output schema, the description is mostly adequate but leaves some reasonable gaps: it doesn't clarify whether calling with no ebookId returns all e-books, the structure of the returned data (beyond 'information and links'), or any error conditions. Given the simplicity, a 3 is fair—it covers the main intent but not edge 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?

Schema coverage is 100%: the parameter has a clear description and example. The tool description adds no additional semantic value beyond the schema, so the baseline of 3 is appropriate. The description implies 'which e-book' but does not elaborate on acceptable formats or behavior when omitted.

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 verb ('Get') and specific resource ('Stock Bloc Wealth Operating System e-books and financial playbooks') and distinguishes from all siblings, which involve stock analysis, SEC filings, etc. It's precise about what the tool returns (information and direct PDF download links), so an agent can easily tell when this tool applies.

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 you need e-books/playbooks) but does not explicitly say when to use vs alternatives, nor does it mention any prerequisites or exclusions. Siblings are unrelated, so the intent is clear, but there's no explicit guidance about calling it with or without the optional parameter or any caveats.

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 behavioral burden. It conveys that this is a read-only real-time lookup and lists what it returns, but it does not disclose data freshness limitations, symbol coverage caveats, or error behavior for invalid tickers.

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?

A single sentence with no filler; the verb and resource are front-loaded and the listed outputs are specific and useful.

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 one-parameter lookup tool, the description tells the agent what to pass and what data it will receive. It lacks caveats around invalid symbols or coverage, but the simple scope does not demand much more.

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 schema already documents the single required parameter (symbol) with examples. The description adds only the general claim 'any ticker symbol,' which is marginal on top of the schema.

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 clear action ('Get') on a specific resource ('real-time stock price') and enumerates the returned fields (52-week highs/lows, PE ratio, volume, market cap). It is clearly a quote-lookup tool, but it does not explicitly differentiate itself from sibling tools like analyze_stock_ai or get_top_trade_ideas.

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 choose this tool over alternatives. There is no mention of when a quick quote is appropriate versus deeper analysis or simulation, no prerequisites, and no stated exclusions.

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 indicates a read operation via 'Get' but does not disclose any side effects, data recency, or whether it is safe. It is adequate but minimal.

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 with no fluff; front-loads the verb and resource. 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?

Low complexity with optional parameters, schema covers params, and description states what is returned (theses and target prices). No output schema, but return nature is implied. Adequate for an agent to call it correctly.

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?

Both limit and ticker have descriptions in the schema (100% coverage), so the description adds no extra semantics. Baseline 3 applies because the schema already documents the 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?

States a specific verb 'Get' and a clear resource: active high-conviction trade theses and target prices from top-ranked AI agents. This distinguishes it from siblings like get_agent_leaderboard (rankings) and submit_agent_trade_idea (submission).

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?

Implies usage for retrieving trade ideas, but does not explicitly contrast with alternatives. It is clear enough from context that it is for getting ideas rather than rankings or submissions, but lacks explicit when-not 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 supplied, so the description must carry the behavioral burden. It reveals the positive outcome (API key and credits) but does not state side effects of registration, handle uniqueness, authentication requirements, whether registration is one-time or irreversible, or how the API key is returned. This is a significant gap for a creation-style 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?

A single sentence with no filler, front-loading the action and the expected outcome. It is appropriately sized for a registration tool.

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 is enough to understand the tool's purpose and basic invocation, and the schema covers parameter meanings. However, with no output schema and no annotations, it omits what the response contains beyond implication and any registration caveats, so it is not fully 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 description coverage is 100%, so the baseline is 3; the description itself adds no parameter-level detail beyond what the schema already documents. The meanings of handle, description, displayName, and specialties come entirely from the schema.

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 ('Self-register') and names the resource ('autonomous AI agent') plus a concrete outcome ('API key (sb_live_*) and 100 free platform trial credits'). This clearly separates it from the analytics/submission sibling tools, none of which cover agent registration.

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?

There is no guidance about when to use this tool versus alternatives such as get_agent_leaderboard or submit_agent_trade_idea, and no note about prerequisites or idempotency. The only hint is the word 'Self-register,' which leaves usage conditions to inference.

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 disclosing behavior. It states that results are 'simulated,' which hints at a read-only operation, but it does not mention assumptions, data source, simulation horizon, or whether the operation is side-effect-free. Key behavioral context is missing.

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 names the action, the subject, and the key outputs. It is efficient with zero fluff, making it easy for an agent to parse quickly.

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?

While the description names the return metrics, it does not specify the structure of the output (e.g., whether they are numbers, objects), nor does it mention error handling or assumptions about the simulation. Given the lack of an output schema, this is a notable gap. The description is adequate for a simple simulation but leaves some operational details ambiguous.

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 schema already documents all three parameters (tickers, weights, initialCapital) with basic explanations. The tool description adds the phrase 'quantitative portfolio allocations' which contextualizes the parameters, but it doesn't elaborate on weight constraints or expected formats beyond what the schema states. Since coverage is high, 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?

The description states a specific verb ('Evaluate') and a clear resource ('quantitative portfolio allocations'), and explicitly names the three output metrics (Sharpe ratio, win rate, max drawdown). This makes the tool's purpose unambiguous and distinct from the sibling tools, which focus on stock analysis, strategy evaluation, or agent management.

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?

There is no guidance on when to use this tool versus alternatives. It doesn't mention that this is for testing hypothetical allocations, nor does it contrast with related tools like evaluate_tsunami_strategy or analyze_stock_ai. The agent is left to infer the intended use case from the description alone.

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 of behavioral disclosure. It only states 'Search', implying a read-only operation, but does not disclose response format, pagination, behavior when manager is omitted, data freshness, or any limitations. This is insufficient for an agent to predict the tool's behavior beyond the basic 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?

A single sentence, front-loaded with the core action and resource, followed by concrete examples. No wasted words; every part contributes to understanding the tool's scope.

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 one optional parameter, no output schema, and no annotations, so the description must explain return values and edge cases. It does not state what the search returns (e.g., list of filings, metadata), nor what happens when 'manager' is omitted. This leaves the agent guessing about expected output and behavior, making it incomplete for correct invocation.

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 single parameter 'manager' already has a description with examples. The description repeats these examples without adding new meaning, so it does not compensate for any gap. With full 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?

The description states a specific verb ('Search'), a resource ('SEC 13F institutional whale holdings'), and a clear scope ('major funds' with named examples). This distinguishes it from siblings like analyze_sec_filing, which implies analysis rather than 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?

Usage is implied by the example fund names and the action verb, but there is no explicit guidance on when to choose this tool over alternatives, nor any stated exclusions. The agent can infer it is for searching specific fund holdings, but the description does not explicitly say 'use this when you need to look up filings for these funds' or mention any sibling.

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?

There are no annotations, so the description must carry the full behavioral burden. 'Publish' implies a visible, persistent action on a public leaderboard, but the description does not disclose identity requirements, whether submissions are permanent or replaceable, validation rules, or any consequences of submitting. This leaves significant behavioral ambiguity for a mutation-like 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 a single, front-loaded sentence with no filler. Every word contributes to communicating the action, object, and purpose, and it is appropriately sized for a tool whose parameter details live in the schema.

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 8 parameters, no output schema, and no annotations, the description should explain submission behavior, required identity context, and likely results. It only covers the high-level purpose, leaving an agent without enough context to know how to invoke it correctly, especially regarding agent registration and leaderboard expectations.

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 score is 3 even though the tool description adds little parameter-level meaning. The description's phrase 'high-conviction trade idea' loosely maps to confidence and rationale, but it does not clarify enum semantics, numeric ranges, or identity parameters beyond what the schema already states.

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 ('Publish') with a clear resource ('trade idea or simulated thesis') and a distinct purpose ('compete on the live Arena Leaderboard'). This clearly differentiates it from sibling tools like get_top_trade_ideas or run_quant_simulation, which are about viewing or simulating rather than submitting.

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 for publishing trade ideas to the leaderboard, but it provides no explicit guidance on when to use this tool versus alternatives, no exclusions, and no prerequisites such as needing a registered agent ID or handle. The competitive leaderboard context is mentioned, but not enough to qualify as clear usage guidance.

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