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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 (readOnlyHint, idempotentHint, non-destructive) are consistent. Description adds detailed behavioral context: walks the order book ladder, returns top_of_book, VWAP fill price, slippage, shares_filled, verdict, and for baskets: per-leg fill detail, thin_legs, forced_directional_risk. No contradictions.

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?

Description is thorough and contains all necessary information, but is dense and could benefit from more structuring (e.g., bullet points). However, key information is front-loaded, and every sentence earns its place given complexity.

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 complexity (two modes, multiple return values) and no output schema, the description fully explains all outputs, edge cases (thin_legs, forced_directional_risk), and usage context. The agent can act correctly without missing information.

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 description coverage is 100%, but the description adds value by explaining the REQUIRES condition (market or event), default logic for side in basket mode, and how size_usd is interpreted differently for buys vs sells and basket notional. Provides context 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?

Clearly states the tool performs a 'realizable-vs-theoretical edge check' against live order-book depth. Distinguishes single-market and basket modes, lists specific output fields, and differentiates from sibling tools 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 instructs 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. Provides rationale about theoretical overround not being capturable and partial basket fills causing directional risk.

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

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes, but some overlap exists between ask_pipeworx, ask_pipeworx_grounded, and deep_research, which all query Pipeworx data in different modes. However, their descriptions clearly differentiate them. Similarly, memory and subscription tools are separate. Overall, an agent can distinguish tools with moderate effort.

Naming Consistency4/5

Tool names mostly follow verb_noun pattern with snake_case, such as ask_pipeworx, compare_entities, generate_llms_txt. However, a few tools like 'datasets', 'metadata', and 'query' are single nouns, breaking the pattern. Overall, naming is consistent enough for an agent to predict behavior.

Tool Count3/5

33 tools is above the typical range for an MCP server, but the server covers a wide domain (SEC, FRED, FDA, prediction markets, etc.) with specialized tools. The count is borderline heavy but justifiable given the scope. Some tools like memory and subscription management add to the count but serve necessary auxiliary functions.

Completeness4/5

The tool surface is comprehensive for its intended domain of structured data querying and analysis. It covers data retrieval, entity resolution, comparison, news, subscriptions, and memory. Minor gaps include dependency scanning only for npm and lack of direct web search, but meta-tools like ask_pipeworx fill many needs. Overall, it supports common workflows well.

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