Foresea Forecasting

foresea_analyze_market

Call this when the user mentions a specific prediction market by URL, slug, or ticker — or asks whether a particular market is over/underpriced. Good triggers: "Is this Polymarket fair?", "What's the edge on kalshi:XXXXX?", "Should I buy/sell this market?", user pastes a Polymarket or Kalshi URL. Fetches the live price, gathers evidence, forecasts, computes model-vs-market edge, and returns a recommendation. Use foresea_forecast instead when there is no specific live market — just a general probability question. Example: platform="polymarket", slug="fed-rate-cut-march-2026" → {model_probability, market_probability, edge, stance, recommendation, thesis}.

Input Schema

TableJSON Schema

Name	Required	Default
`slug`	No
`skills`	No
`ticker`	No
`variant`	No	variant0_neutral_baseline
`platform`	No
`question`	No
`market_id`	No
`tool_loop`	No
`builtin_skills`	No
`evidence_top_k`	No
`max_tool_steps`	No
`ground_in_record`	No
`market_probability`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

TDQS

A4.1/5.0

Behavior4/5

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.

Conciseness4/5

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.

Completeness3/5

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.

Parameters2/5

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.

Purpose5/5

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.

Usage Guidelines5/5

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.

All Foresea Forecasting tools Inspect

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TDQS

A3.6/5.0

Disambiguation3/5

Some tools have overlapping purposes: foresea_edge_board and foresea_scan_markets both return markets ranked by model-vs-market edge, and foresea_forecast vs foresea_debate_market both provide probabilities for a question. Descriptions help differentiate (specific vs. broad, quick vs. deep) but the boundaries are not always clear.

Naming Consistency4/5

All tools share the 'foresea_' prefix and use lowercase with underscores, which provides consistency. However, the remainder mixes verb-noun phrases (analyze_market, scan_markets) with bare nouns (orderbook, price_history) and compound nouns (market_leaderboard, polymarket_meta), creating minor stylistic inconsistency.

Tool Count4/5

17 tools is slightly above the typical well-scoped range of 3-15, but the server covers a broad domain (forecasting, market analysis, data feeds, portfolio optimization, track record). A couple of tools are redundant (edge_board vs scan_markets) and could be consolidated, but overall the count is reasonable.

Completeness4/5

The tool set covers the core analytics workflow: general forecasting, specific market analysis, opportunity scanning, market data (quotes, orderbook, history, trades), portfolio allocation, and performance evaluation. Minor gaps exist (e.g., no tool for detailed market rules or user portfolio tracking), but these are likely outside the intended scope.

foresea_analyze_market

Input Schema

Output Schema

TDQS

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TDQS

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