Skip to main content
Glama

Market Recap

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.

TDQS

A4.9/5.0
Behavior5/5

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

Even though annotations already declare readOnly/idempotent/non-destructive behavior, the description adds substantial behavioral detail: it walks the order-book ladder, returns verdicts (clean|degraded|cannot_fill), and explains that partial basket fills turn an arb into unhedged directional risk. This goes well beyond the annotations and paints a clear picture of what the tool does and its limitations.

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 dense, and it uses clear SINGLE-MARKET/BASKET sectioning and all-caps reminders for critical information. While every sentence adds value, the length and emphasis patterns make it slightly less scannable than an ideal description; a light reorganization into bullets would improve readability without losing content.

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?

With no output schema, the description fully compensates by enumerating the returned fields per mode (top_of_book, vwap_fill_price, slippage_pp, shares_filled, verdict; theoretical_sum, capture_ratio, profit_usd, thin_legs[], etc.). It also covers the critical edge cases and risk context, making it self-sufficient for an agent to decide and invoke correctly.

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%, but the description goes far beyond the schema by explaining mode-dependent semantics: market vs event modes, how side and size_usd are interpreted differently in single-market vs basket mode, and the default/clamping behavior. It also clarifies the mutual-exclusivity requirement, which the schema does not enforce.

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 opening sentence 'Realizable-vs-theoretical edge check against live CLOB order-book depth' gives a specific verb and resource, and the tool is clearly distinguished from sibling tools like polymarket_arbitrage and polymarket_edges by focusing on fill risk/execution quality. The description also states its exact role in the workflow, removing any ambiguity about purpose.

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 says 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500', providing concrete triggers. It also explains when not to rely on theoretical edges and why partial basket fills are dangerous, which is strong usage guidance.

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

All Market Recap toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation2/5

Many tools overlap in purpose (e.g., multiple Polymarket analysis tools, multiple AI visibility tools, ask_pipeworx vs deep_research). Agents will have difficulty choosing the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names use a mix of styles (snake_case, descriptive phrases) without a consistent verb_noun pattern. For example, 'ask_pipeworx' and 'bet_research' have different naming conventions. This inconsistency makes it harder for agents to predict tool names.

Tool Count2/5

32 tools is on the high side for a single server. Many tools could be merged (e.g., multiple polymarket tools). The count feels excessive for the scope, causing cognitive load and potential selection errors.

Completeness3/5

The tool set covers a wide range of domains (prediction markets, company data, fact-checking, etc.) but has notable gaps (e.g., limited entity types for company/drug only). Redundancy in some areas makes the set feel bloated rather than complete.

Resources