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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. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds behavioral details: '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)'. Also explains basket mode returns and forced directional risk. No contradiction with annotations. The description goes beyond the safety profile to explain the internal logic and risk implications.

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 long but well-structured: starts with overall purpose, then separated sections for single-market and basket modes using ALL-CAPS headers. Each section lists inputs and outputs. Front-loaded with key purpose. Some redundancy (e.g., size_usd description repeated in schema and text) but overall efficient for the complexity. No wasted sentences.

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 no output schema, the description thoroughly explains return values for both modes: lists top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, verdict for single-market; theoretical_sum, realizable_sum, capture_ratio, profit_usd, per-leg fill detail, thin_legs, max_clean_notional_usd, forced_directional_risk for basket. Also explains the risk of partial fills. Covers all necessary context for safe and effective use.

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% (all 4 parameters described in schema). Description adds meaning: explains that size_usd is 'max spend on buys, target proceeds on sells' for single-market and 'settlement notional — shares per leg' for basket. Clarifies that side default for basket is 'auto — sell if partition sum > 1, buy if < 1'. Also notes clamping range for size_usd (10–1,000,000). This significantly enriches the schema descriptions.

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 the tool does a 'realizable-vs-theoretical edge check against live CLOB order-book depth' in two modes (single-market and basket). It distinguishes from siblings by explicitly saying 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. The verb 'check' and resource 'fill risk' are specific.

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. Provides requirements: 'REQUIRES one of market (single-market mode) or event (basket/partition mode)'. Also explains when not to use or why it's needed: 'theoretical overround on thin books is not capturable' and warns about partial basket fills converting arb to unhedged directional position, which is the dominant loss mode.

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

A3.9/5.0
Disambiguation3/5

The toolset is mostly organized by clear subdomains, but there are multiple overlapping entry points: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer research questions and the beta version is currently identical to the stable router. Detailed descriptions reduce confusion, but an agent could still reasonably pick the wrong one for a given task. The entity, memory, and subscription tools are more clearly separated.

Naming Consistency3/5

Names are consistently lower_snake_case and readable, but the set mixes verb-led names (compare_entities, resolve_entity, validate_claim) with noun-led names (entity_profile, polymarket_edges, pipeworx_trending) and some odd pairings like ai_visibility_check vs scan_competitor_ai_presence. No chaotic camelCase or inconsistent separators, but the convention is not uniform enough for a strong score.

Tool Count2/5

34 tools is past the 25+ threshold and the surface spans many unrelated domains: structured data lookup, prediction markets, AI visibility marketing, city open data, npm dependency checking, llms.txt generation, memory, and subscriptions. Each tool may be individually useful, but the collection feels like a platform dump rather than a tightly scoped server. A more focused server would split off prediction markets, AI visibility, and utility tools.

Completeness4/5

The main data-research workflow is well covered: discovery, routing, grounded answering, deep research, entity resolution, profiles, comparisons, recent changes, claim validation, and search-within-results are all present. Prediction-market analysis, memory, and subscription lifecycles also have no major dead ends. Minor gaps exist, such as no write/update path for open data and no subscription option for AI-visibility monitoring, but these are not central to the apparent core purpose.

Resources