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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?

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description reveals internal behavior: it walks the order-book ladder, computes VWAP fill price, slippage, and returns a verdict. It warns about thin books and forced directional risk. This adds significant behavioral context that annotations alone do not provide.

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 well-structured with clear section headers (SINGLE-MARKET, BASKET) and uses capitals for key requirements. It is somewhat verbose but every sentence adds necessary detail for a complex tool. It could be slightly more terse but remains effective.

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 tool has no output schema, the description thoroughly explains return fields (top_of_book, vwap_fill_price, etc.) and covers edge cases like thin_legs and forced_directional_risk. It also mentions usage context (above ~$500, arb signals). The description is complete for an agent to understand inputs, outputs, and risks.

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 baseline is 3. The description adds meaning by explaining the dual-mode interpretation of side (auto for baskets) and clarifying that size_usd is settlement notional for baskets. This extra context justifies a score of 4.

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 opens with a precise verb+resource: 'Realizable-vs-theoretical edge check' against 'live CLOB order-book depth'. It distinguishes itself from siblings by explicitly stating to use this before acting on polymarket_arbitrage or polymarket_edges signals, and it outlines two distinct modes (single-market and basket) with clear definitions.

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 provides explicit when-to-use guidance: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It also explains the rationale (partial basket fills convert arb into unhedged directional position) and states the required parameters (market or event), effectively telling the agent when to choose 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.

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TDQS

A3.6/5.0
Disambiguation2/5

Several tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta (currently identical), and ask_pipeworx_grounded are three variants of the same router, while bet_research, polymarket_edges, and polymarket_arbitrage all target prediction-market opportunities. The descriptions are detailed, but an agent must read extensively to avoid selecting the wrong tool within each cluster.

Naming Consistency2/5

Naming is a mix of conventions: get_*/search_* for NASA tools, ask_pipeworx_* and polymarket_* family prefixes, plus one-off names like entity_profile, bet_research, deep_research, recent_changes, and scan_dependency. There is no consistent verb_noun or family-wide pattern, making tool selection unpredictable despite each individual name being readable.

Tool Count2/5

36 tools is heavy for a server named Nasa, and only 5 of them are actually NASA-related; the rest form a sprawling general data-research, prediction-market, memory, and subscription toolkit. The count is borderline defensible for a broad data assistant, but it is clearly unjustified under the server's stated NASA identity.

Completeness3/5

As a general data-research assistant the surface is quite complete: discovery, routing, grounded verification, entity profiles, comparisons, memory, subscriptions, and feedback are all covered. As a NASA server, however, there are notable gaps—no EONET events, Earth observation, exoplanet archive, or TLE/mission-specific data—and the large non-NASA tool surface does not fill those gaps.