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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

A4.8/5.0
Behavior5/5

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

The description discloses behavior beyond annotations, such as walking the ladder, returning a verdict (clean|degraded|cannot_fill), and explaining that partial basket fills lead to unhedged directional positions. Annotations already indicate readOnlyHint, idempotentHint, so the description adds valuable context about output fields and risk flags.

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 moderately long but well-structured, starting with a clear one-sentence purpose, then separating single-market and basket modes with bold headers, and concluding with use-case guidance. Every sentence adds value, though it could be slightly more concise without losing meaning.

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 documents return fields for both modes (e.g., top_of_book, vwap_fill_price, slippage_pp, verdict, thin_legs, forced_directional_risk). It also explains the dominant loss mode from partial fills, making the tool complete for its intended use.

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%, so baseline is 3. The description adds significant meaning beyond the schema, e.g., explaining that size_usd for basket is 'settlement notional S (shares per leg; each share pays $1),' and clarifying side defaults in basket mode. This extra context justifies a score above baseline.

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 it is a 'realizable-vs-theoretical edge check against live CLOB order-book depth.' It explicitly distinguishes two modes: single-market and basket/partition, and the verb 'check' with the resource 'order-book depth' is specific. It differentiates from siblings like polymarket_arbitrage and polymarket_edges by being a pre-trade risk assessment.

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?

The description explicitly states when to use the tool: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It explains the consequence of not using it (partial basket fills convert an arb into an unhedged directional position), providing clear context for when to choose this tool over alternatives.

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

A3.5/5.0
Disambiguation1/5

Several tools are nearly indistinguishable: ask_pipeworx and ask_pipeworx_beta are explicitly identical in behavior, and ask_pipeworx_grounded overlaps heavily with them. Additionally, entity_profile, compare_entities, deep_research, and validate_claim all cover similar company/factual research territory, creating frequent selection ambiguity.

Naming Consistency2/5

Naming mixes multiple conventions: descriptive lowercase phrases (ai_visibility_check, compare_entities, valid claim) coexist with verb_noun (search_documents, recent_rules) and inconsistent underscores (ask_pipeworx vs ask_pipeworx_grounded, resolve_entity). There is no single recognizable pattern.

Tool Count1/5

The server is named 'Federal Register' but only 3 of 34 tools (search_documents, recent_rules, get_document) relate to that domain. The other 31 tools form a sprawling Pipeworx data and prediction-market suite, making the count extreme and inappropriate for the declared purpose.

Completeness2/5

For the stated Federal Register domain, the surface is minimal: search, recent listing, and single-document retrieval, with no docket browsing, full-text search within documents, or agency-specific navigation. The broader Pipeworx capability set is comprehensive but irrelevant to the server's name, leaving obvious gaps for the actual purpose.