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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

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, which the description does not contradict. The description adds substantial behavioral context: it explains the tool walks the order book ladder, returns fill details like slippage and max fillable, and warns about forced directional risk in basket mode. This goes beyond the annotations' safety profile.

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 bullet points and bolded terms, making it easy to scan. It front-loads the core purpose and then details modes, parameters, returns, and risks. While fairly long, every sentence adds value, so it earns its length.

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, no output schema), the description covers everything: it explains both modes, lists return values for each, describes parameter defaults, and provides risk warnings. The absence of an output schema is mitigated by the detailed return value documentation.

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 coverage is 100%, but the description adds significant meaning: it explains the 'auto' default for side in basket mode, describes 'event' as basket mode and 'market' as single-market mode, and gives default and clamp range for size_usd. This enriches the parameter understanding beyond the schema.

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 states 'Realizable-vs-theoretical edge check against live CLOB order-book depth' which clearly identifies the tool's purpose. It distinguishes itself from sibling tools like polymarket_arbitrage and polymarket_edges by explicitly recommending its use 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 explicitly states when to use the tool: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. It also explains why partial basket fills are risky, providing clear guidance on when not to rely on theoretical values.

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

Many tools have overlapping purposes, especially the pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) which all route to the same data sources. Additionally, the server includes unrelated meta-tools (remember/recall/forget, generate_llms_txt, pipeworx_feedback) that have no clear boundaries with the poverty data tools, and betting tools that seem out of place. The core poverty tools (get_poverty, get_poverty_regional, list_reference) are distinct, but the rest creates significant confusion.

Naming Consistency2/5

Tool names are a mix of styles: some use snake_case (get_poverty, list_reference, suggest_questions), some use camelCase (ask_pipeworx, bet_research, scan_competitor_ai_presence), and others are single words (recall, remember, forget, subscribe). The naming pattern is highly inconsistent, making it hard to predict related tool names.

Tool Count2/5

With 34 tools, this server is heavily overloaded for a 'Worldbank Poverty' server. The majority of tools are unrelated to poverty (Polymarket betting, AI marketing, npm package checks, LLM visibility). The core poverty functionality could be served by 3-5 tools, but instead the server includes dozens of extra tools from a generic data platform, making the count inappropriate for the stated domain.

Completeness4/5

For the poverty data domain, the tool surface is actually quite complete: get_poverty for country-level data, get_poverty_regional for aggregations, and list_reference for metadata. The only minor gap is a lack of a tool for comparing poverty across countries directly, but that is easy to work around by calling get_poverty multiple times. The extra meta-tools do not affect poverty data completeness.