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 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, 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 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.