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Imf Portwatch

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations (readOnlyHint, etc.) indicate safe read operation. Description adds critical behavioral details: walks the order book ladder, returns verdict (clean/degraded/cannot_fill), forces directional risk, and explains basket mode risks. 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?

Description is dense (≈250 words) but well-structured with uppercase headings for modes. Front-loaded with core function. Every sentence adds value, though slightly verbose for the complexity. Could be trimmed slightly 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 high complexity (two modes, many return fields, no output schema), description fully explains return values, failure modes, and usage context. Covers forced directional risk, thin legs, and max clean notional. No gaps for agent to infer.

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 description adds substantial meaning: explains side default for basket (auto), size_usd interpretation for basket (settlement notional), and clamps (10–1,000,000). Clarifies single-market vs basket parameter usage beyond 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?

Description clearly states tool checks realizable vs theoretical edge against live order book depth. Differentiates between single-market and basket modes with specific verbs and resources. Distinguishes from siblings like polymarket_arbitrage and polymarket_edges by focusing on risk assessment before acting on those 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?

Explicitly states when to use: before acting on polymarket_arbitrage signals or polymarket_edges trades above ~$500. Explains why (theoretical overround not capturable, partial fills cause unhedged directional positions). Specifies required parameters: must provide market or event.

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

A4.2/5.0
Disambiguation4/5

Most tools have clear distinct purposes, but the three ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) are very similar, and there is some overlap between bet_research and polymarket_edges. Overall, the majority are well-differentiated.

Naming Consistency5/5

Tool names follow a consistent snake_case verb_noun pattern with only minor deviations (e.g., 'chokepoints_list' vs 'chokepoint_daily_traffic'). The naming convention is predictable and clear.

Tool Count2/5

At 37 tools, the surface is overly large for a focused server. Many tools are meta-tools that could have been consolidated, and the count exceeds the recommended range (25+), making it feel heavy and unwieldy.

Completeness5/5

The server covers maritime chokepoints comprehensively (list, status, daily, compare, disruptions), and the Pipeworx-based tools provide broad coverage across financial, drug, prediction market, and general query domains. There are no obvious gaps for the stated scope.

Resources