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Balldontlie

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

A5/5.0
Behavior5/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds significant context: ladder walking, verdict options, thin legs, forced directional risk, and the warning that partial basket fills convert an arb into an unhedged directional position. It thoroughly discloses behavioral traits beyond annotations.

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

Conciseness5/5

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

The description is well-structured with sections (REQUIRES, SINGLE-MARKET, BASKET, USE THIS) and front-loaded with the core purpose. Every sentence adds value, and while detailed, it is concise for the complexity of the tool. No waste.

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?

Given the complexity (two modes, multiple return fields), no output schema exists, but the description comprehensively details all return values for both modes, including edge cases (thin_legs, forced_directional_risk). It provides sufficient context for an agent to use the tool correctly without needing additional information.

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

Parameters5/5

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

Schema description coverage is 100%. The description adds meaning to parameters: explains single-market vs. basket modes, auto-detection of side in basket mode, interpretation of size_usd (max spend on buys, target proceeds on sells). Goes beyond schema definitions to clarify usage semantics.

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 performs a realizable-vs-theoretical edge check against live CLOB depth, distinguishes between single-market and basket modes, and lists specific return values. It effectively differentiates from siblings like polymarket_arbitrage and polymarket_edges.

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 says when to use the tool ('before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'), provides rationale for why it's needed, and implies when not to use (small trades). Gives clear context for agent decision-making.

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

A3.5/5.0
Disambiguation2/5

Many tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all performing similar data queries. Similarly, bet_research, polymarket_arbitrage, polymarket_edges, and polymarket_fill_risk cover the same betting domain. Players/player, teams/team, and games/game also blur distinctions.

Naming Consistency2/5

Naming styles are inconsistent: some use verb_noun (e.g., validate_claim, discover_tools), others are plain nouns (e.g., player, team, stats), and some are individual verbs (e.g., forget, recall). There's no predictable pattern.

Tool Count2/5

With 39 tools, the set is large and spans multiple unrelated domains (NBA stats, betting, general data lookup, memory). Given the server name 'Balldontlie' suggests NBA focus, the number is excessive and many tools feel out of place.

Completeness2/5

For an NBA stats server, the tool surface is incomplete (missing play-by-play, advanced stats, season leaders, etc.). As a general data server, it relies on meta-tools like ask_pipeworx rather than dedicated tools, so coverage is indirect and not comprehensive.

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