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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.

Schema Changelog

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

  1. Added
  2. Removed
  3. Added

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already mark it read-only, open-world, and idempotent. The description adds value by detailing internal behavior like 'walks the ladder', return fields (e.g., top_of_book, vwap_fill_price, slippage_pp, verdict), and warns about forced directional risk and partial basket fills being the 'dominant loss mode'. 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?

The description is long but well-structured with clear sections (SINGLE-MARKET, BASKET) and bullet-like enumeration of return fields. Every sentence adds value, though some redundancy exists (e.g., repeating 'US$1' for basket shares). Still, it's front-loaded with the core purpose.

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?

Despite no output schema, the description fully enumerates all return fields, including edge cases like thin_legs[], max_clean_notional_usd, and forced_directional_risk. It also explains the tool's role in the arb-bot workflow and accounts for complex behaviors like partial fills and unhedged positions.

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% (baseline 3), but the description adds rich semantics: explains size_usd differs by mode (max spend on buys vs target proceeds on sells, and settlement notional for baskets), clarifies side defaults and mode-specific options, and emphasizes the market/event exclusivity (REQUIRES one of). These details go far 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 clearly states the tool performs a 'Realizable-vs-theoretical edge check against live CLOB order-book depth' and distinguishes single-market and basket modes with specific behaviors. It 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 states 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. Provides clear context for single-market vs basket, includes warnings about thin books and partial fills converting arb to directional positions.

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

A3.7/5.0
Disambiguation3/5

Many tools have overlapping purposes, especially the Pipeworx query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) which all serve similar data retrieval needs. Additionally, entity_profile, compare_entities, and recent_changes overlap in providing company information. Polymarket tools also have overlapping analysis functions. This can cause confusion for agents.

Naming Consistency2/5

Tool names are inconsistent in style and convention. Some use underscores (ai_visibility_check, ask_pipeworx), others are single words (forget, recall), and many lack a clear verb_noun pattern (pipeworx_feedback, polymarket_edges). This mixture of naming conventions reduces predictability.

Tool Count3/5

With 32 tools, the count is on the higher side but appropriate given the broad scope covering multiple domains (Pipeworx data, Polymarket betting, ACLED events, npm scanning, memory, etc.). However, some areas have only one or two tools, which feels sparse, and the overall set could be trimmed or better organized.

Completeness3/5

The tool set covers many domains but has notable gaps. For ACLED, only search and count tools exist without any update/delete capabilities. For Pipeworx, the tools are heavily read-focused with no apparent write operations. The broad scope makes completeness hard to assess, but some obvious lifecycle operations are missing.

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