Skip to main content
Glama

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

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable beyond-annotation context: the tool returns fill details, verdicts, and warns about directional risk from partial fills. 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?

The description is well-structured with sections ('SINGLE-MARKET', 'BASKET'), bullet points, and clear defaults. While verbose, every sentence is informative and earns its place. Could be slightly more concise but front-loads the core purpose effectively.

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 thoroughly explains all return fields for both modes (e.g., top_of_book, vwap_fill_price, slippage_pp, capture_ratio, thin_legs). It addresses edge cases like forced directional risk and max clean notional, making it complete for a complex arbitrage-checking tool.

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% with basic descriptions for all 4 parameters. The tool description adds significant context: explains mode-specific behavior, default side auto-detection in basket mode, and the dual interpretation of size_usd (spend vs. proceeds vs. settlement notional). This goes beyond what the schema provides.

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 specifies the tool's function as a 'realizable-vs-theoretical edge check against live CLOB order-book depth' and distinguishes two modes (single-market and basket). It differentiates from sibling tools like polymarket_arbitrage and polymarket_edges by being a pre-trade risk check rather than a signal generator.

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: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500'. Provides reasoning about partial basket fills converting arbs into unhedged positions, giving clear decision criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

The tool set mixes two completely different domains: setlist.fm (12 tools) and Pipeworx data services (30+ tools). An agent cannot easily distinguish which tools belong to the server's primary purpose, leading to confusion and misselection.

Naming Consistency2/5

Setlist.fm tools use consistent verb_noun patterns (artist, artist_search, artist_setlists), but the majority of tools follow no unified convention: some use snake_case (ai_visibility_check), others use mixed case (ask_pipeworx), creating an inconsistent naming landscape.

Tool Count2/5

43 tools is excessive for a setlist.fm API. Only about 12 are relevant; the remaining 31 are unrelated and bloat the tool surface, making it hard to navigate and maintain.

Completeness4/5

For the setlist.fm domain, the tools cover search, retrieval, and user data comprehensively (artists, setlists, venues, cities, countries, users). Minor gaps exist (e.g., no update/delete operations), but core workflows are supported.