Skip to main content
Glama

Bts

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

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the tool is clearly non-destructive. The description adds extensive behavioral context: it performs a read-only check, walks the order book ladder, returns verdicts like 'clean', 'degraded', 'cannot_fill', and warns about partial fill risks. No contradiction with annotations.

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 lengthy but well-structured: a clear one-sentence purpose, then REQUIREMENTS, then detailed breakdown of both modes, then usage guidance. Every sentence adds value, though some repetition could be trimmed. It is front-loaded with the most critical information.

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 lacking an output schema, the description fully compensates by detailing return values for both single-market and basket modes, including edge cases like thin legs and forced directional risk. It covers all behavioral nuances needed for an AI to invoke the tool correctly.

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 coverage is 100%, so baseline is 3. The description adds significant value: interprets size_usd for buys vs sells and basket mode, explains side default roles, and clarifies market/event mode selection. This extra context justifies a 4, though it stops short of 5 because some details (e.g., exact format of market slug) are omitted.

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 the tool's purpose: 'Realizable-vs-theoretical edge check against live CLOB order-book depth.' It explicitly differentiates from sibling tools by specifying when to use it: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.'

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 provides explicit usage guidance: when to use (before arb signals, trades above $500) and why (theoretical overround on thin books not capturable, partial basket fills cause directional risk). It also explains the two modes and their triggers.

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

All Bts 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

A4/5.0
Disambiguation4/5

Most tools have distinct purposes with clear descriptions, but the multiple ask_pipeworx variants and several Polymarket tools could cause initial confusion. An agent reading carefully can differentiate them, but the similarity in themes requires attention.

Naming Consistency3/5

Names use a mix of conventions: verb_noun (ask_pipeworx, compare_entities), noun_noun (entity_profile, dataset_columns), and single verbs (remember, forget). While some subgroups have internal consistency (e.g., polymarket_*), there is no overall predictable pattern.

Tool Count2/5

With 34 tools, the count exceeds the 'too many' threshold of 25. While the server is comprehensive, the large number of highly specific tools (especially for prediction markets) feels overwhelming and could confuse agents.

Completeness4/5

The tool set covers a wide range of capabilities: data querying, entity resolution, company analysis, prediction markets, memory, subscriptions, and validation. Minor gaps exist (e.g., no data writing tools), but for the read-heavy analytical purpose, it is nearly complete.

Resources