Foresea Forecasting

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

No annotations provided, so the description carries full burden. It discloses the workflow: fetches price, gathers evidence, computes edge, returns recommendation. Missing details on potential side effects or rate limits, but adequate for an analysis 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?

Well-structured with front-loaded purpose, triggers, example, and alternative. Slightly verbose but every sentence adds value. Could be tightened without losing clarity.

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 13 parameters and no schema descriptions, the description is incomplete. It explains key parameters and output shape, but many parameters are undocumented. Output schema exists, so return values are covered.

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 0%, so the description must compensate. It explains platform, slug, and ticker via example, but omits 10 other parameters (skills, variant, question, etc.), leaving their meaning unclear.

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 purpose: analyzing a specific prediction market by URL, slug, or ticker, answering whether it's over/underpriced. It provides specific triggers and example input, distinguishing it from sibling tools.

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?

Explicitly states when to call (user mentions specific market by URL/slug/ticker) and when not to use (use foresea_forecast for general probability questions). Names the alternative 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?

With no annotations, the description fully discloses critical behaviors: quotes include fetched_at and age_seconds for freshness checking, both venues rate-limit hard (so quotes may not be live), one bad ref fails only that entry while the rest succeed, and there is a 50-ref limit. These are exactly the behavioral traits an agent needs to know, well beyond any schema information.

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 focused paragraph that front-loads the purpose, then adds essential operational details (format, rate limits, error handling, limit) without redundancy. Every sentence contributes value, and the example output further clarifies behavior without adding 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?

Despite moderate complexity (batch processing, partial failures, rate limits, output shape), the description is complete: it explains input format, limits, failure semantics, freshness metadata, and gives an example output shape. The presence of an output schema is supplemented by the example, making the tool fully understandable without further investigation.

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 only lists 'refs' without any description (0% coverage). The description compensates fully by defining the ref format as 'platform:ident' with concrete examples (kalshi:KXFED-25JUN-H, polymarket:some-market-slug) and specifying the 50-ref limit. It turns an opaque array of strings into a well-understood parameter.

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 current price/volume for several markets at once, with the verb 'Call this when' and specific scenarios. It explicitly contrasts with foresea_analyze_market, making its unique purpose unmistakable.

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 provides explicit guidance on when to use the tool ('a watchlist, a portfolio, "check on these 5 markets"') and when not to ('instead of calling foresea_analyze_market once per market'), directly naming the alternative. This is exemplary 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 burden. It discloses the two possible response states ('status: running' or full report), advises to call again if not done, and explains that the research may still be running server-side. It could mention error handling or retry behavior, but is quite transparent for a simple polling 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 efficient and front-loaded with the trigger condition. Every sentence earns its place: the trigger, the response behavior, the restriction against speculative calls, and an illustrative example. No filler or redundant 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?

For a tool with one parameter and an output schema, the description covers all necessary context: when to invoke, what to pass, how to interpret the result, and what to do if still running. The output schema handles detailed return structure, so the description 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?

The schema only defines client_run_key as a string with 0% description coverage, so the description must compensate. It explains the key must come from a prior foresea_analyze_market call and provides an example usage, adding crucial context beyond the raw 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 clearly specifies the tool's purpose: to check the status of an asynchronous research run initiated by foresea_analyze_market after it timed out or errored. It also differentiates itself from siblings by focusing on polling/checking rather than initiating or analyzing.

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?

Explicitly states when to use (after foresea_analyze_market times out or errors with a client_run_key) and when not to use ('Do not call this speculatively'). It also instructs to only use keys from prior calls, leaving no ambiguity about the trigger 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 provided, the description carries the full burden for behavioral disclosure. It only describes the action but omits any side effects, permissions, rate limits, or whether the tool is read-only or mutating. For a multi-agent debate tool, it is unclear if it makes external calls, requires authentication, or has costs. The description fails to disclose these behavioral traits 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?

The description is a single, front-loaded sentence that packs the action, roles, and purpose without any wasted words. It is concise and well-structured, prioritizing the core functionality.

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 tool's complexity (multi-agent debate) and the presence of 4 parameters with zero documentation, the description is incomplete. It does not explain parameter semantics, behavioral traits, or how the debate is structured. Although an output schema exists (covering return values), it does not compensate for the missing guidance on inputs and operational behavior. The description is insufficient for a tool with such nuanced inputs.

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 0%, meaning the description explains none of the parameters. It only references a 'forecasting question' which maps to the 'question' parameter, but this adds no meaning beyond the schema itself. It does not clarify 'market_probability', 'resolution_criteria', or 'platform' defaults (e.g., 'Market'), leaving agents without understanding of how to use these fields.

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 ('Conduct an adversarial multi-agent debate') and its purpose ('cross-examine evidence and isolate blind spots'), with specific roles (Bull, Bear, Chief Risk Judge) that distinguish it from sibling tools like foresea_analyze_market or foresea_forecast. The verb+resource is specific and unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for forecasting questions but does not explicitly state when to use this tool over alternatives, nor does it provide exclusions or mention any sibling tools. It offers context about the target domain but lacks direct guidance on when to prefer it over other analysis or forecasting tools.

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 fully handles transparency. It explains the tool returns ranked markets with directions, implied odds, historical significance, and multi-model comparison. No negative behaviors mentioned, but acceptable for a read-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 concise, front-loaded with action triggers, and every sentence adds useful information without 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?

Given zero parameters and a detailed output description, the description is complete. Sibling tools are listed for context, and the description covers what the tool does and what it returns.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

There are no parameters, so the schema coverage is 100%. The description adds value by explaining what the tool returns, which is sufficient for a parameterless tool.

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 purpose: providing top trading opportunities with directions and historical backing. It distinguishes from siblings by focusing on 'model-vs-market disagreement' and the 'edge board', and includes specific user triggers.

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 explicitly says 'Call this when the user wants...' and provides good trigger phrases. It implies usage context but does not explicitly state when not to use or list alternatives.

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 and adequately discloses the tool's behavior: it returns the trading active flag and operational hours/schedule. No side effects, permissions, or rate limits are mentioned, but for a simple read-only status check with zero parameters, this is sufficient. Slightly more detail (e.g., real-time vs. cached) would improve 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, well-structured sentence that front-loads the action ('Call this to check') and immediately lists the specific outputs. Every word earns its place, with no fluff or repetition of the function name.

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 tool with an output schema present, the description fully covers the tool's purpose and what it returns. It is complete and correctly scoped, matching the low complexity of the operation. No additional context is necessary to invoke the tool 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?

The tool has zero parameters, so the schema provides complete coverage. Per the rubric, a baseline of 4 is appropriate since no parameter explanation is needed. The description focuses on the output semantics, which is the right emphasis for a no-argument operation.

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 ('check') and resource ('Kalshi exchange operational status'), and explicitly names what it returns ('trading active flag', 'operational hours/schedule'). This clearly distinguishes it from sibling tools like foresea_analyze_market or foresea_orderbook, which focus on market data rather than exchange-level 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 phrase 'Call this to check...' provides clear contextual guidance for when to use the tool. It does not explicitly mention alternatives or exclusions, but given the tool's unique focus on exchange status among siblings, the use case is unambiguous. A brief note about not using it for market-specific data would have earned a 5.

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 adds that the feed is 'real-time' and 'unified' and lists contents, but offers no insight into ordering, caching, rate limits, error behavior, or how 'edge' is computed. This is minimal additional context beyond the tool's name and title.

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 immediately identifies the action and resource, then efficiently lists the feed's components. No words are wasted, and the structure is easy to scan. Every element adds value without 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 tool with two optional parameters and an output schema, the description adequately explains what the feed contains, but it omits crucial context about parameter usage (especially 'min_edge') and any caveats about real-time data. The presence of many sibling tools raises the need for clearer operational context, which is partially satisfied by the output schema but not by the description.

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 description coverage is 0%, yet the description makes no mention of the 'limit' or 'min_edge' parameters. While 'limit' is intuitive and 'min_edge' hints at a threshold, the description does not clarify the semantics of 'edge' in this trading/feed context or explain how parameters affect results. The description offers no compensation for the schema's lack of parameter documentation.

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 ('Fetch') and resource ('real-time unified Foresea Alpha & Agent Feed'), then details the feed's components (edge signals, agent trades & theses, leaderboard standings), which clearly differentiates it from siblings like foresea_market_leaderboard or foresea_edge_board. This exceeds a generic paraphrase and tells the agent exactly what the tool provides.

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 explicit guidance is given on when to use this tool versus siblings. There is no mention of alternatives, exclusions, or scenarios where another tool would be preferred. The description only states what the feed combines, leaving the agent to infer usage context from the tool name and 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, the description fully discloses the tool's behavior: it returns a calibrated YES/NO probability (or numeric/date range), rationale, news evidence, and optionally market analysis (model probability vs market). It also states it handles multiple question types. No hidden side effects are implied.

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 informative and well-structured, starting with when to call, then detailing output, an example, and supported question types. It is somewhat lengthy but front-loaded with the most critical information. A minor reduction in examples could improve conciseness.

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 tool's complexity (13 params, no annotations, but an output schema exists), the description covers the core use case and return format well. However, it leaves many parameters undocumented, which is a gap for an agent attempting to use the tool optimally. The output schema mitigates some completeness concerns.

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 0%, so the description must compensate. It explains only a few parameters (question, market_probability, market_url) and alludes to options/categories for multiple-choice, but 13 parameters exist (e.g., variant, description, question_type, resolution_criteria) with no explanation. The description does not add sufficient meaning beyond the schema for most 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 clearly states the tool is for forecasting questions about probability, likelihood, or whether something will happen, with explicit trigger examples and a detailed return specification (calibrated probability, rationale, evidence). The verb 'forecast' matches the resource, and the scope is well-defined, though it does not explicitly differentiate from siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear triggers (e.g., 'Will X happen?', 'What are the chances of Y?') and advises passing market_probability/market_url for market edge analysis. However, it does not explicitly state when NOT to use this tool or compare it to sibling tools like foresea_analyze_market or foresea_scan_markets, which would strengthen 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 behavioral disclosure burden. It does communicate that data is real-time, sports-focused, and sourced from Kalshi, and it names two distinct request modes. However, it does not disclose behavior when no args are provided, possible data_type values, or potential limitations/errors, which matters because all parameters are optional.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no filler. The primary purpose is front-loaded, and the parameter usage follows immediately. Every sentence contributes necessary guidance.

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 output schema exists and the parameter set is small, the description covers the essential call patterns well. It explains the main two usage modes and the data source. Minor omissions like full data_type enumeration and handling of empty arguments are present, but they do not prevent a competent agent from invoking the tool correctly in most 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 description coverage is 0%, so the description must compensate. It does explain event_ticker and milestone_id roles and gives a concrete data_type value, which is useful. But it only mentions one data_type value and does not clarify allowed values, defaults, or whether event_ticker and milestone_id are mutually exclusive, leaving significant gaps.

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 identifies the tool's action ('fetch'), resource ('real-time sports game statistics, play-by-play data, and live event feeds'), and source ('from Kalshi'). It is specific enough to distinguish this from siblings like orderbook or forecast tools without needing to inspect their schemas.

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 actionable usage guidance: use event_ticker for event charts, or milestone_id with data_type='game_stats' for play-by-play. It provides clear context for when to use this tool, though it does not explicitly state exclusions or compare against sibling tools.

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 indicates a read operation ('fetch'), which is appropriate. However, it does not clarify what 'top profitable' means, how rankings are determined, whether data is real-time, or if there are limitations (e.g., pagination, rate limits). The description is minimally adequate but lacks depth for a tool with no annotation safety net.

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, 14-word sentence that is efficient and front-loaded. It conveys the core action without unnecessary words. However, it could be slightly expanded to include parameter guidance without losing conciseness.

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 tool's simplicity (one optional parameter, output schema exists), the description is partially complete. The output schema likely documents return values, so that omission is acceptable. However, the description lacks context about when this leaderboard is useful relative to siblings, and it does not explain the 'limit' parameter. The completeness is adequate for a straightforward fetch but not comprehensive.

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 0%, meaning the input schema provides no descriptions for the single parameter 'limit' (optional, default 20). The tool description does not mention or explain this parameter at all. It fails to add any meaning beyond the schema, leaving the agent to guess the parameter's purpose and behavior.

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 'the top profitable prediction market trader leaderboard and rankings from Polymarket.' This is a specific verb-resource combination that distinguishes it from sibling tools like foresea_analyze_market (analyzes individual markets) and foresea_scan_markets (searches markets). The purpose is unambiguous and well-differentiated.

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 guidance is provided on when to use this tool versus alternatives, such as when to use foresea_scan_markets for market discovery or foresea_analyze_market for deep analysis. There are no prerequisites, exclusions, or context given. The description simply says 'Call this to fetch...' without any decision-making support.

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 and does so thoroughly. It discloses that the endpoint returns a fixed slice of at most 100 tags, is not alphabetically or activity-ordered, may contain one-off or misspelled entries, and that presence/absence does not indicate activity or usability. This is rich behavioral context beyond the schema.

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 concise (three sentences) and front-loads the core purpose immediately. Every sentence adds value: the first states the action and output, the second explains the pagination limit and ordering, the third clarifies the implications for tag usage. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (0 params) and the presence of an output schema, the description covers all necessary context: what it returns, the limits, and how to interpret the data. An agent can call this tool with full understanding of what to expect, including edge cases like misspellings and non-exhaustive coverage.

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 schema trivially covers 100%. The baseline for 0 params is 4; the description does not need to explain parameter semantics and does not attempt to, which 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 opens with a specific action ('sample Polymarket's category vocabulary') and specifies the output ('each entry is a label and the slug that identifies it'). It clearly distinguishes the tool's purpose from the broader taxonomy and from any classification role, making it unambiguous what the tool does.

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 explicitly states when to call it ('Call this to sample...') and provides clear exclusions: it is not the full taxonomy, not a classification system, and absence/presence of a tag has specific meaning. It does not name alternative tools, but given the sibling set, the context is sufficient for an agent to know this is the vocabulary-sampling 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?

With no annotations, the description must carry the full burden. It clearly indicates a calculation (non-destructive) but does not disclose any side effects, dependencies on live data, or limitations. The phrase 'Grade A/B' hints at filtering criteria but adds no behavior beyond the verb 'Calculate.' It is not misleading but lacks depth.

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 efficiently conveys the core action and scope. While it lacks parameter details, it is appropriately concise with no redundancy or 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?

The description provides the primary purpose but omits necessary context for a portfolio optimization tool with three parameters and no annotations. It does not explain how the parameters interact, what an optimal allocation entails, or any constraints. Given the tool's complexity and the sparse schema, the description is insufficient for a new agent to use 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?

Schema description coverage is 0%, and the description provides no meaning for 'min_edge,' 'bankroll_usd,' or 'kelly_fraction.' The term 'Fractional Kelly' hints at kelly_fraction but does not explain any parameter. The description fails to compensate for the undocumented 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 specific verb ('Calculate') and a clear resource ('optimal mathematical Fractional Kelly capital allocations and position sizes') with scope ('across live Grade A/B prediction market opportunities'). It clearly distinguishes from sibling tools that focus on analysis, scanning, or data 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?

Usage is implied by the phrase 'across live Grade A/B prediction market opportunities,' suggesting it is for active markets, but there is no explicit guidance on when to use this tool versus alternatives, no exclusions, and no mention of prerequisites or sequencing with other tools.

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 transparency burden. It conveys that the data is live and includes bid/ask depth, and it names both supported venues. It does not disclose response behavior, staleness, or any side-effect profile, though the output schema covers return values.

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 one direct, front-loaded sentence with no filler. The examples are relevant and compact, and the sentence earns every word.

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 orderbook tool, the description covers the core argument and use case, and the output schema supplies return structure. Still, the undocumented optional platform parameter and lack of explicit alternative guidance leave minor but real 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 coverage is 0%, so the description must compensate. It explains ticker_or_token well with concrete Kalshi and Polymarket examples, but the optional 'platform' parameter is never described. Its meaning, allowed values, and relationship to ticker_or_token remain undocumented.

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 action ('fetch'), a precise object ('live bids and asks orderbook depth'), and concrete examples for both supported inputs. This makes it clearly distinct from sibling tools like price_history, recent_trades, or live_data.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly states the intended scenario: obtain current bid/ask depth for a Kalshi ticker or Polymarket YES-token ID. However, it does not explicitly mention alternatives or when not to use this tool, so it stops short of full routing guidance.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It transparently explains the scope of each target, including what data is intentionally omitted. This gives the agent a strong sense of the tool's behavior, though it does not cover edge cases like missing market_id or invalid targets.

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 main purpose and target list, then organized into target-specific clarifications. Every sentence adds useful information, though the format is slightly longer than necessary because it repeats the target names in the explanatory lines.

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 two parameters and an output schema, but with no parameter documentation and no annotations, the description needs to cover invocation details more fully. It explains three modes well but leaves 'teams' undefined and never clarifies when market_id should be supplied, especially for 'comments'.

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 0%, so the description must compensate. It explains valid target values and their meanings, but it never names or explains the market_id parameter, and the 'teams' target is listed without any detail. The description partially bridges the gap but leaves key parameter semantics ambiguous.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action ('fetch Polymarket metadata') and precisely identifies the resource types handled: series listings, comments, sports leagues, and teams. It also differentiates what the tool does not return, making its purpose unmistakable even among many sibling tools.

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 clear context for when to call the tool by enumerating the four target modes and what each returns. It does not explicitly name alternative sibling tools or say 'use X instead', but it does provide useful exclusion boundaries such as 'not the events themselves' and 'not commenters' profiles'.

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. It states 'retrieve' which suggests a read operation, but does not disclose any behavioral details such as data range limits, pagination, rate limits, or authentication requirements. The description adds the concept of OHLC candlesticks, but lacks depth for a tool without annotation support.

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 with no extraneous content. It is front-loaded and efficient.

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 presence of an output schema and two parameters, the description is too brief. It does not explain the output format, the difference between the two parameters, or how this tool fits with historical data needs. With many sibling tools, more context would be beneficial.

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 0%, meaning the description does not document any parameters. The description mentions 'ticker_or_market' in examples but does not explain the 'series_ticker' parameter or clarify how the two parameters interact. With zero schema descriptions, the description fails to add sufficient meaning.

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 historical price series or OHLC candlesticks for a market, with concrete examples (Kalshi ticker, Polymarket slug). This distinguishes it from real-time data tools like foresea_live_data, though not explicitly. The verb 'retrieve' and resource 'price history' are specific.

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 use for historical data but provides no explicit guidance on when to prefer this over sibling tools like foresea_analyze_market or foresea_scan_markets. It does not exclude any use cases or mention prerequisites.

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 discloses that it returns recent public trades with prices, sizes, and timestamps. However, it does not explain the meaning of 'recent' (time window), the behavior of the limit parameter, or the valid values for platform. The lack of enum definitions in the schema compounds this 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, efficient sentence that conveys the core purpose. It is front-loaded with the action. However, its brevity leads to missing parameter and usage context. It earns every word but could benefit from a second sentence on parameters.

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 tool has 3 parameters with no schema descriptions and no enum constraints, the description is insufficient to fully guide the agent. It omits parameter details, valid platform values, and the behavior of the 'limit' field. The existence of an output schema partially compensates, but the description remains incomplete.

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 description adds no information about the three parameters (limit, platform, ticker_or_token). With 0% schema description coverage, the agent must infer from parameter names alone. The description does not explain defaults, valid values, or how to use them for filtering.

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 (fetch), the resource (recent public executed trades/trade tape), and the data types included (prices, sizes, timestamps). It also specifies the supported platforms (Kalshi or Polymarket), distinguishing it from sibling tools like order book or price history.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives (e.g., when should the agent use foresea_orderbook instead?). There is no mention of prerequisites, context, or 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 describes the return format (model probability, market price, edge) and includes an example output, implying a read-only operation. However, it does not explicitly state non-destructiveness or any safety aspects.

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 well-structured with a clear purpose, trigger list, output description, and example. It is concise but could be slightly tighter by removing redundant wording.

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 5 parameters and no annotations, the description provides adequate context for usage and output but lacks parameter documentation. The output schema exists but its content is unknown; the description covers the gist of the return fields.

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 0%. The description only mentions platform and min_edge in an example, leaving limit, query, and evidence_top_k unexplained. With low coverage, the description should add meaning for all parameters, but it does not.

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?

Clearly states the tool finds mispriced or interesting markets, with specific trigger phrases and a distinction from evaluating a specific market. Names the sibling tool foresea_analyze_market as the 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?

Explicitly says when to use (user wants to find trading opportunities) and when not to (evaluating a specific market, directs to foresea_analyze_market). Provides example triggers and a sample invocation.

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 clearly describes the output metrics and structure, but does not mention any potential side effects, data source, or update frequency. Since the tool has no parameters and is read-only, the behavioral disclosure is good but not exhaustive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences: the first provides usage conditions and trigger phrases, the second lists the outputs. It is compact, front-loaded, and every sentence is meaningful.

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 tool has zero parameters, no required inputs, and an output schema exists, the description is complete. It clearly states the purpose, triggers, and return metrics without missing essential information.

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. The description adds value by explaining what the return values contain, which goes beyond the empty input 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 explicitly states the tool returns accuracy, Brier score, calibration (ECE), and skill-vs-market broken down by time horizon. It also provides specific trigger phrases, making the purpose very clear and distinguishing it from sibling tools.

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 starts with 'Call this when the user asks how reliable or accurate Foresea is' and lists concrete trigger examples like 'How good is Foresea?' and 'What's the track record?'. This gives explicit guidance on when to use this tool vs alternatives.

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 present, the description carries the burden of behavioral disclosure and does well by stating 'No account or write access,' establishing the tool as read-only and safe. It also discloses the self-describing behavior of omitting operation to discover available operations and schemas. It doesn't cover error behavior or pagination, but the core behavioral profile 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?

The description is compact and front-loaded with the main read scope, followed immediately by the essential discovery instruction and safety note. Every clause adds distinct value, and the long data-type list is dense but not redundant.

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 appears to be a dynamic operation-driven venue-data API, and the description provides the key bootstrapping instruction: omit operation to discover schemas. The presence of an output schema helps cover return-value expectations. Some ambiguity remains around body/platform/parameters, but the discovery mechanism likely closes that 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?

Schema description coverage is 0%, so the description must compensate for all parameters. The only parameter guidance given is that 'operation' can be omitted to discover names and schemas; body, platform, and parameters remain undefined. This leaves the agent guessing about the request structure.

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 starts with a clear, specific verb and resource: 'Read public historical markets/candles/trades...' and lists concrete data categories. It clearly identifies this as a read-only venue-data tool, though it doesn't explicitly differentiate it from overlapping siblings like foresea_price_history or foresea_recent_trades.

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 an actionable instruction: 'Omit operation to discover operation names and schemas,' which tells an agent how to bootstrap usage. It also implies this tool is for public read access, but it does not provide explicit when-to-use vs. alternatives guidance or exclusions for sibling tools.

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

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

The description implies a read-only operation with 'retrieve' and 'calculation', but it does not explicitly state side effects, rate limits, or data source limitations. No annotations are provided, so the description carries the full burden, but it is not misleading. Some transparency is present but incomplete.

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 but dense sentence. It lists many models and capabilities, which adds detail but is somewhat verbose. The structure is clear and not redundant, earning a high score, though it could be trimmed 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?

An output schema exists, so return values need no explanation. However, the description lacks essential context about parameter semantics and when to use the tool relative to siblings. The mention of 'weather prediction markets' gives a hint of use case, but overall completeness is low due to missing parameter guidance.

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 0% and the description does not explain the parameters `station_or_query` or `target_date`. Although station examples (KNYC, KMDW) are given in context of bias correction, they are not linked to the parameters. The description completely fails to clarify parameter meaning, format, or constraints.

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 (retrieve) and the resource (neural model weather forecasts), and lists specific models (MetNet, ECMWF, GFS, GraphCast) to distinguish it from generic forecast tools. It also mentions unique capabilities like bias correction and strike bracket probability, making the purpose highly specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not provide any guidance on when to use this tool versus alternatives like foresea_weather_radar or foresea_forecast. It lacks explicit conditions or contrasts that would help an agent select this tool over siblings.

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

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

With no annotations, the description carries the full burden. It discloses the output (ranked mispricings, probabilities, edge) and implies a read-only scan, but it does not explicitly state that no trades are placed or mention any side effects, limitations, or safety characteristics.

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 run-on sentence packed with specific tickers, model names, and jargon. It is not front-loaded or structured; it would benefit from splitting into a purpose statement and a details section. Every word does not earn 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?

The tool has an output schema (true), so the description needn't detail return structures, yet it does summarize the key outputs (mispricings, probabilities, edge). The single optional parameter is not explained, but the core behavior and expected result are clear enough for a basic call.

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 0%, so the description must explain the target_date parameter. It does not mention it at all. The agent is left to infer that target_date is an optional date filter, but the description provides zero guidance on its format, meaning, or effect.

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 'Scan' and the resource: live temperature and weather prediction markets, listing specific tickers and models. It distinguishes itself from siblings like foresea_scan_markets (generic) and foresea_weather_forecast (likely just forecasting) by specifying the comparative mispricing 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 for finding mispricings in weather markets but does not explicitly state when to use this tool versus alternatives or provide exclusions. No contrast with sibling tools is given, leaving the decision to inference.

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