Stock Bloc

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 usefully discloses the payment mechanism ($0.25 USDC via x402 on Base), which is real behavioral context an agent needs, but it says nothing about latency, failure modes, authentication, or what 'structured financial intelligence' actually contains.

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?

One sentence, front-loaded with the verb and resource. The trailing agent name and pricing clause is slightly cluttered but is legitimate information rather than 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?

With no output schema, the description should characterize the return value, but 'structured financial intelligence' is vague and leaves the agent guessing about shape and depth. Pricing is covered, but result semantics are not.

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 all three parameters (ticker, question, filingType) are already documented in the schema, including the enum for filingType. The description adds no format or semantic detail beyond the schema, so the baseline of 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?

States a specific verb (Analyze) plus the resource (SEC filings) and enumerates the supported filing types (10-K, 10-Q, 8-K), so the agent knows exactly what the tool produces. It does not explicitly distinguish itself from siblings like search_13f_whale_filings, but the resource is concrete enough to route on.

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 choose this over siblings such as search_13f_whale_filings or analyze_stock_ai, and no stated prerequisites. The only hint is the optional 'question' field, which implies a Q&A use case but is not framed as 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?

No annotations are provided, so the description carries the full behavioral burden. It does not say whether the call is read-only (implied but unstated), how heavy/long-running the analysis is, whether results are cached or rate-limited, or what the response contains. Only '(Free)' hints at a cost characteristic.

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?

One front-loaded sentence with no filler; the action and scope come first. The trailing '(Free)' parenthetical is slightly awkward placement but conveys real value, so the sentence remains efficient overall.

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 heavy analysis tool with no output schema and no annotations, the description lists the three analysis dimensions (AI, fundamental, technical), which helps set expectations. But it leaves the return shape, depth, and any constraints unspecified, so an agent still has notable gaps.

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 a single parameter at 100% schema description coverage, the schema already documents 'symbol' with examples (NVDA, AMZN, PLTR). The description's 'any stock ticker' merely restates the schema's meaning and adds no format or edge-case detail. 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 clear verb (Run) and resource (AI market analysis, fundamental metrics, technical signals) scoped to any stock ticker. An agent can tell this is an analysis/report tool rather than a raw quote lookup. However, it never names or contrasts with siblings like get_stock_quote or run_quant_simulation, which blur its boundary.

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 when-to-use guidance beyond 'for any stock ticker'. It does not say when to prefer this over get_stock_quote (simpler quote) or run_quant_simulation (quant modeling), nor any exclusions or prerequisites. The '(Free)' tag hints at cost tradeoffs but is not framed as selection 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 exist, so the description carries the full burden. It does disclose a genuinely useful behavioral fact: the call is paid ($0.10 USDC via x402 on Base), which affects whether an agent should invoke it. However, it says nothing about authorization requirements, whether the allocation must sum to 1, validation failures, or whether the operation is a pure computation with no side 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?

Two compact sentences that front-load the core purpose, then the outputs and the price. Nothing is padded; the only knock is that the ticker list and payment detail are crammed into the same sentence as the return metrics.

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 helpfully names the returned metrics (Alpha, Sharpe, Win Rate, Drawdown), and it flags the cost. But for a nested-object, paid compute tool it omits allocation constraints, horizon/benchmark defaults, and any sense of runtime or failure modes.

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%, with enums and defaults documented inline (benchmark, horizonDays, riskTolerance) and an example allocation map, so the schema does the heavy lifting. The description adds only the concrete benchmark ticker set, not syntax or constraint detail beyond the schema. 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 states a specific verb and resource ('Evaluate a quantitative portfolio strategy') and names the exact watchlist being evaluated, so the agent knows precisely what computation happens. It does not differentiate itself from the nearby sibling run_quant_simulation, which leaves ambiguity about which to pick for quant-style work.

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 when-to-use or when-not-to-use guidance and never references any alternative tool. With run_quant_simulation sitting in the same sibling list, the absence of routing guidance is a real gap for an agent choosing between the two.

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 behavioral disclosure burden. It usefully identifies the returned data types and that access is free, but it omits freshness, ranking period, and other runtime characteristics for a retrieval endpoint.

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 front-loaded sentence with no filler. Every clause lists concrete return data, so it is appropriately sized for a simple retrieval 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 tool is simple: one optional parameter, no output schema, and no annotations. The description names the main returned fields well enough for invocation, but it could mention the ranking window or refresh cadence to be 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%: the sole limit parameter is already documented with its default. The description adds no additional meaning about the parameter, so the 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?

States a specific verb and resource: fetch top ranked Stock Bloc AI agents. The enumerated payload (win rates, alpha returns, badges, active trade recommendations) further distinguishes it, and no sibling tool covers the same leaderboard resource.

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 when-to-use guidance, no alternatives, and no exclusions. The parenthetical '(Free)' is pricing information, not usage guidance, so an agent gets no help deciding when this tool is appropriate.

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 does not state whether this is a read-only operation (though it clearly is), what the output looks like, whether it is cached, or any rate limits. It mentions the feeds covered but not the behavior of the check itself or what constitutes a 'stale' flag.

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, front-loaded with the core action and the specific fields checked, followed by the scope of feeds. Every word earns its place; there is 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?

For a zero-parameter, read-only status tool with no annotations and no output schema, the description covers what is checked and which feeds are included. However, it does not specify the return format or what a caller should do with the result, leaving some ambiguity about how to interpret the 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?

Parameter count is 0, so baseline is 4. The description provides no parameter information, which is appropriate since there are none. No penalty or bonus beyond 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?

States a specific verb and resource: 'Check pipeline data freshness, updated_at timestamps, and stale boolean flags' across three named feeds. This is clear and distinct from siblings like get_stock_quote or search_13f_whale_filings, which retrieve specific content rather than infrastructure status. However, the parenthetical '(Free)' is confusing — it's unclear whether it means the tool is free to use, or that it only covers 'Free' tier feeds, and it distracts from the core 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?

The description implies this tool should be used to check data freshness, which suggests its use case, but it never explicitly says when to call it versus alternatives. No exclusions or prerequisites are stated. A user might not know if this should be called before every data request, or only when staleness is suspected.

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 behavioral burden. It discloses that it returns direct PDF download links and that content is free, which is useful, but says nothing about whether it requires ebookId, what happens when it is omitted (the parameter is optional), whether any authentication is needed, or rate limits on the 'direct download links'.

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 front-loaded sentence with no filler; the resource and the return payload come first. It is efficient, though it packs scope, audience, and a fee note into one clause rather than structuring them.

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 tool with a fully documented single parameter and no output schema, the description is mostly adequate. However, it never resolves the central ambiguity that ebookId is optional, leaving an agent unsure whether omitting it lists all e-books or fails.

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 ebookId with concrete examples. The description adds no syntax, format, or behavioral meaning to the parameter, so 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?

States a specific verb (Get) and a specific resource (Stock Bloc Wealth Operating System e-books and financial playbooks), plus what it returns (information and direct PDF download links). No sibling tool performs anything similar, so discrimination is trivially satisfied, but the scope is clear enough to call it without opening the schema.

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 notes the material is '(Free)' but gives no guidance on when to use this versus alternatives, and none of the sibling tools overlap. There is no statement about prerequisites, or when an agent should reach for this tool at all.

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 must carry behavior, and it does disclose a meaningful trait: $0.05 USDC pay-per-call via x402 on Base. It also implies read-only and 'audited' provenance. However, it says nothing about pagination behavior (despite a cursor param), rate limits, or latency.

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 front-loaded sentence with no padding; the core purpose leads and the payment note trails. Slightly dense with the parenthetical billing detail, but nothing is wasted.

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 4-param paginated tool with no output schema and no annotations, the description covers purpose and cost but omits what a snapshot record contains, how pagination terminates, and how methodologyVersion affects results. Adequate but with visible gaps.

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 all four parameters (ticker, limit, cursor, methodologyVersion) are already documented in the schema. The description adds no parameter-level detail beyond confirming the ticker scope, so 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?

States a specific verb+resource ('Get audited historical SB Score quantitative snapshots and performance tracking') and scopes it to any stock ticker with concrete return windows (30d/60d/90d). This is clearly distinct from siblings like get_stock_quote or analyze_stock_ai, though it never names an alternative explicitly.

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 statement of when to use this versus siblings such as get_stock_quote or get_data_status, and no prerequisites beyond the price note. The cost disclosure implies 'only if you're willing to pay' but never gives explicit usage conditions.

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 discloses real-time data freshness and the payment mechanism (x402 pay-per-call, $0.01 USDC on Base), which is genuinely useful behavioral context. However it doesn't state rate limits, error behavior for invalid tickers, or the response shape, leaving meaningful gaps.

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, no filler, with the pricing detail parenthetically isolated at the end so the core purpose is front-loaded. Slightly dense due to packing data fields and payment metadata together.

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 read-only tool with no output schema and no annotations, the description covers the fields returned and the payment model, which is decent, but leaves unclear whether the pay-per-call is automatic, blocking, or requires setup, and provides no guidance relative to its many siblings.

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% with examples (NVDA, AAPL, TSLA, BTC), so the schema already documents the parameter fully. The description adds no format, casing, or validation constraints beyond that. 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?

States a specific verb ('Get') and resource ('real-time verified stock price, 52-week range, PE ratio, volume, market cap for any ticker symbol') and is distinguishable from siblings like analyze_stock_ai or get_top_trade_ideas that perform analysis rather than raw quote 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 scope says 'for any ticker symbol,' which implies usage, but it never states when to use this over analyze_stock_ai or get_top_trade_ideas, nor when it would be inappropriate. Usage must be inferred.

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 behavioral burden. It does disclose real behavioral constraints — only 'active' theses from 'top-ranked' agents are returned — but says nothing about authentication needs, rate limits, pagination, or the shape of the response, leaving meaningful gaps.

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 front-loaded sentence with no filler; the key noun phrase ('active high-conviction trade theses and target prices') comes first. The trailing '(Free)' parenthetical is minor but slightly awkward.

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 two-parameter read tool with no output schema, the description covers the core purpose and the scoping notion of 'active' and 'top-ranked'. It stops short of explaining result ordering, pagination, or return structure, which an agent would need to interpret the output confidently.

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 both parameters (limit with default 10, ticker filter) are already fully documented in the schema. The description adds no additional parameter meaning such as ticker format rules or what 'top-ranked' thresholds apply, so the 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?

States a specific verb and resource: retrieves active high-conviction trade theses with target prices. The implicit contrast with submit_agent_trade_idea (submission vs. retrieval) is clear from the verb, but the description never names a sibling to differentiate explicitly.

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 only usage hint is the '(Free)' tag, which signals a pricing tier but not when to use this tool versus alternatives such as analyze_stock_ai or get_agent_leaderboard. No when-to-use or 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.

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

With no annotations, the description must carry the behavioral load, and it does disclose a genuinely useful trait: a $0.10 USDC x402 pay-per-call on Base, which tells an agent about cost and payment/auth requirements. However, it omits simulation methodology, data window, determinism, and error conditions.

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 front-loaded sentence names the action first, then the returns, then the pricing detail at the end where it belongs. It is tight with no filler, though the trailing parenthetical packs two unrelated facts (price and payment rail).

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 annotations and no output schema, the description partially compensates by naming the three return metrics. But for a quantitative simulation tool it lacks the lookback period, data source, and failure behavior an agent would need to call it confidently.

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 tickers, weights, and initialCapital. The description only restates 'allocations' generically and adds no format or constraint detail beyond the schema, making the baseline 3 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 uses a specific verb (Evaluate) plus resource (quantitative portfolio allocations) and names the concrete outputs (Sharpe ratio, win rate, max drawdown). It is easy to distinguish from most siblings like get_stock_quote, though it doesn't differentiate itself from the similarly evaluative evaluate_tsunami_strategy.

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 explicit when-to-use or when-not-to-use guidance. The description implies backtesting/simulation context but never says what scenario selects this tool over evaluate_tsunami_strategy or analyze_stock_ai, leaving routing entirely 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 the full burden, but it does disclose a real behavioral trait: the $0.10 USDC x402 pay-per-call pricing on Base. It omits return format, freshness/latency of 'live SEC filings', and any rate limits or failure behavior.

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 front-loaded sentence with the resource first and pricing last; nothing is redundant. The parenthetical pricing is dense but earns its place as the only cost signal.

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 should describe what a search returns (fund list, holdings, filing dates) but does not. Scope, examples, and cost are covered, leaving the result shape and the behavior when 'manager' is omitted unstated.

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 single 'manager' parameter has 100% schema coverage with its own examples, so the baseline is 3. The description's parenthetical fund list duplicates the schema's example values rather than adding new semantics (e.g. partial matching, case sensitivity, whether omission returns all managers).

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 and resource ('Search SEC 13F institutional whale holdings') and scopes it with named example funds, so an agent understands the domain immediately. It does not explicitly distinguish itself from the sibling 'analyze_sec_filing', which covers similar SEC ground.

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 when-to-use or when-not-to-use guidance, and no routing to alternatives even though a closely related sibling (analyze_sec_filing) exists. The only usage signal is implicit scope from the fund examples.

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 does disclose a real behavioral trait the schema cannot express: a $0.25 USDC x402 pay-per-call charge on Base, which matters for an autonomous agent budgeting calls. It does not cover auth requirements, latency, rate limits, or failure modes, so the disclosure is partial.

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 front-loaded sentence delivers the action, the resource, the outputs, and the payment model, with the alias note last. It is dense but every clause carries information; 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?

With no output schema, the description compensates by naming the return content (structured financial intelligence, revenue quality audits, forensic risks) and the payment requirement. Given the simple 3-parameter schema, this is close to complete; only error behavior and analysis depth remain unstated.

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 all three parameters (ticker, question, filingType) are already documented in the schema. The description adds no per-parameter semantics – e.g. it does not clarify what 'question' should contain or how deep the analysis goes – so the baseline of 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?

States a specific verb (analyze) and resource (SEC filings) and enumerates the concrete filing types (10-K, 10-Q, 8-K) plus the outputs it produces. It also declares itself an alias of analyze_sec_filing, which is a sibling, so an agent can resolve the duplication without opening a schema.

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 resource description – call it to analyze an SEC filing – and the alias note implicitly tells the agent this and analyze_sec_filing are interchangeable. However, there is no explicit when-to-use/when-not guidance against the other analysis siblings such as analyze_stock_ai or search_13f_whale_filings.

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, but it does disclose an important behavioral trait: the $0.10 USDC x402 pay-per-call cost on Base. It omits whether a registered agent ID is required, what publishing does to leaderboard state, or any rate limits, leaving notable gaps for a write operation.

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, front-loaded sentence with no filler; the action and purpose lead, and the pricing detail is relegated to a parenthetical. The parenthetical is dense but informative rather than wasteful.

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 an 8-parameter write tool with no annotations and no output schema, the description covers purpose and cost but says nothing about the required agentID/handle registration flow, success behavior, or failure modes (e.g., payment failure). Adequate but with clear gaps.

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 8 parameters (ticker, action enum, rationale, agentId, etc.). The description adds no parameter-level meaning beyond confirming a thesis is being submitted, so 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 ("Publish") and resource ("trade idea or simulated thesis") with a clear goal (compete on the Arena Leaderboard). It implicitly distinguishes itself from read-side siblings like get_top_trade_ideas, though it never names them explicitly.

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 statement of when to use this versus alternatives such as evaluate_tsunami_strategy or run_quant_simulation, nor any prerequisites despite register_autonomous_agent being a sibling. The only usage context is the implied goal of competing on the leaderboard.

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