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N2yo

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

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is given. The description adds substantial behavioral detail: it walks the order-book ladder, returns specific metrics (top_of_book, vwap_fill_price, slippage_pp, etc.), handles defaults and clamping, and warns about thin_legs and forced_directional_risk—all beyond what annotations provide.

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 a dense single paragraph, but it is well-structured with clear mode separators (SINGLE-MARKET: / BASKET:) and a final usage directive. It is longer than average, but every sentence carries critical information for a complex two-mode tool, so the length is justified.

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 the tool's complexity and lack of an output schema, the description must explain return values, and it does: it lists all key outputs (top_of_book, vwap_fill_price, slippage_pp, shares_filled, max_fillable_usd, verdict for single; theoretical_sum, realizable_sum, capture_ratio, profit_usd, thin_legs, etc. for basket). It also covers parameter semantics, edge cases, and the practical risk of partial fills, making it complete for the agent.

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%, so the baseline is 3, but the description enriches parameter meaning significantly. It explains mode-specific interpretations (e.g., size_usd means 'max spend on buys' in single-market but 'settlement notional S (shares per leg)' in basket mode), defaults ('default buy_yes', 'default auto'), and the clamp range. This goes well beyond the schema's per-parameter 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?

The description opens with 'Realizable-vs-theoretical edge check against live CLOB order-book depth,' which precisely identifies the tool's function and resource. It clearly distinguishes two operating modes (single-market and basket) and differentiates from sibling tools 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?

Explicit guidance is provided: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' It also explains why this tool should be used first, citing the risk of unhedged directional positions from partial fills, which serves as a clear when-not condition.

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

A3.8/5.0
Disambiguation2/5

Multiple tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates (beta is currently identical), and the five Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) require careful reading to distinguish. Additionally, entity_profile, compare_entities, and recent_changes all handle company data, and ai_visibility_check vs scan_competitor_ai_presence are clearly paired. The detailed descriptions help, but an agent will frequently misselect among these clusters.

Naming Consistency3/5

All names are lowercase with underscores, so the style is internally consistent. However, the pattern is mixed: many use verb_noun (get_positions, list_subscriptions, resolve_entity) but several are noun-first or noun-only (entity_profile, polymarket_edges, pipeworx_trending, bet_research). There's no strong verb/noun convention across the set, making the naming pattern less predictable than it could be.

Tool Count2/5

With 34 tools, the set exceeds the 16–25 'heavy' range and sits in the 'too many' band. The server name suggests a focused satellite-tracking service, yet only 3 tools (get_positions, get_visual_passes, whats_above) serve that purpose; the other 31 cover unrelated domains like data research, prediction markets, memory, and subscriptions. Even as a general-purpose research platform, the count feels bloated and unfocused.

Completeness3/5

The tool surface is broad, covering satellite tracking, data research, prediction markets, subscriptions, and memory, and within each cluster the main operations exist (e.g., subscription lifecycle, edge analysis + fill risk). However, the scattered scope creates gaps: there's no direct raw-data fetch tool (everything goes through ask_pipeworx), no general web search, and the presence of unrelated utilities (generate_llms_txt, scan_dependency) suggests the domain boundaries are unclear. For its stated satellite purpose, the satellite tools are thin (no TLE, no catalog, no detailed orbit info).

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