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Horizons Nasa

by io.github.pipeworx-io

Polymarket Fill Risk

polymarket_fill_risk
Read-onlyIdempotent

Realizable-vs-theoretical edge check against live CLOB order-book depth. REQUIRES one of market (single-market mode) or event (basket/partition mode). SINGLE-MARKET: pass a market slug/URL + side (buy_yes|sell_yes|buy_no|sell_no, default buy_yes) + size_usd (default 1000 — max spend on buys, target proceeds on sells); walks the ladder and returns top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, and a verdict (clean|degraded|cannot_fill). BASKET: pass an event slug/URL + side (sell_yes = capture overround by selling every leg, buy_yes = capture underround; default auto from partition sum) + size_usd interpreted as settlement notional S (shares per leg; each share pays $1); returns theoretical_sum vs realizable_sum (top-of-book vs VWAP across all legs), capture_ratio, profit_usd at executed size, per-leg fill detail, thin_legs[], max_clean_notional_usd, and forced_directional_risk naming the legs most likely to strand you unhedged. USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500 — theoretical overround on thin books is not capturable, and partial basket fills convert an arb into an unhedged directional position (the dominant loss mode in real arb-bot P&L).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sideNoSingle-market: buy_yes | sell_yes | buy_no | sell_no (default buy_yes). Basket: sell_yes | buy_yes (default auto — sell if partition sum > 1, buy if < 1).
eventNoBasket mode: event slug or full polymarket.com URL — checks every leg of the partition.
marketNoSingle-market mode: market slug or full polymarket.com URL.
size_usdNoSingle-market: USD to spend (buys) or target proceeds (sells). Basket: settlement notional — shares per leg, each paying $1 at resolution. Default 1000, clamp 10–1,000,000.

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the tool's safety profile is clear. The description adds rich behavioral context: walks the ladder, returns specific fields (top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, verdict), and explains forced directional risk. No contradiction with annotations.

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?

The description is long but every sentence adds value, and it front-loads the core purpose. Minor redundancy could be trimmed, but it remains structured with mode-specific details.

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

Completeness5/5

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

Despite no output schema, the description enumerates all return fields for both modes and covers edge cases (thin legs, forced directional risk). For a complex tool, this is exceptionally complete.

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

Parameters4/5

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

Schema coverage is 100%, so parameters are already documented. The description adds value by explaining the default auto-detection of side in basket mode and how size_usd is interpreted differently for buys vs sells and in basket mode.

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 checks 'realizable-vs-theoretical edge' against live order-book depth, and distinguishes between single-market and basket modes. It explicitly differentiates from siblings like polymarket_arbitrage and polymarket_edges by stating when to use this tool before acting on their signals.

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?

The description gives explicit usage context: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It also warns about consequences of misuse, such as partial basket fills converting an arb into an unhedged directional position.

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

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TDQS

A4/5.0
Disambiguation5/5

Each tool has a clear, distinct purpose with detailed descriptions that differentiate overlapping capabilities (e.g., ask_pipeworx vs deep_research vs ask_pipeworx_grounded vs bet_research). No two tools appear redundant; even similar prediction-market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk) have specific scopes.

Naming Consistency5/5

Tool names consistently use lowercase snake_case (e.g., ai_visibility_check, compare_entities, pipeworx_trending, polymarket_kalshi_spread). Single-word exceptions (ephemeris, lookup, observers, recall, remember, vectors) are common short verbs and do not break the pattern. No mixing of camelCase or other conventions.

Tool Count4/5

35 tools is above the typical 3-15 range, but the server is a comprehensive data platform covering multiple domains (SEC, FDA, FRED, prediction markets, memory, subscriptions, feedback). The count is justified given the breadth; it feels slightly heavy but not bloated or redundant.

Completeness5/5

The tool surface covers the full lifecycle for a data/research platform: discovery (discover_tools, suggest_questions), entity resolution (resolve_entity), lookups (ask_pipeworx, entity_profile), comparison (compare_entities), validation (validate_claim), prediction-market operations (polymarket_*), memory (remember/recall/forget), subscriptions (subscribe/unsubscribe/list_subscriptions/recent_alerts), and meta/feedback (pipeworx_feedback, pipeworx_trending). No obvious gaps for typical workflows.

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