Skip to main content
Glama

Fmi

by io.github.pipeworx-io

Polymarket Arbitrage

polymarket_arbitrage
Read-onlyIdempotent

Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks. Call with NO args for a trending_scan of the top ~200 markets by weekly volume; pass event for the strongest per-event partition_check, or topic for a themed cross-event scan. event (recommended for a specific market): pass a Polymarket event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k"; walks child markets, checks date-axis / threshold-axis ordering AND computes the partition_check (sum of YES prices across mutually-exclusive legs — should ≈1; deviations >3pp emit a BUY/SELL EVERY LEG signal). topic (for cross-event scanning): pass a seed question like "Strait of Hormuz traffic returns to normal" or "Fed rate decision"; searches related events across the platform, flattens markets, runs the comparator on the union. Cross-event mode catches "...by May 31" vs "...by Jun 30" patterns that single-event misses. SEMANTIC ANCHOR: cross-event pairs require ≥0.30 Jaccard similarity on question tokens (prevents Powell-Fed-Pause being paired with Powell-DOJ-probe); skipped_low_similarity surfaces the rejected pair count. PARTITION FILTER: drops will-person-X / will-manager-Y / will-someone-else- placeholder slugs; partitions with >20% placeholder fraction return null arb signal. Response: opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context), and in event mode partition_check{sum_yes_prices, gap_from_1, placeholders_filtered, suggested_trade}. FILL CHECK: when the partition signal fires, arbitrage.fill_check prices it against live CLOB depth (theoretical_edge_pp_at_book vs realizable_edge_pp at 1000 shares/leg, thin_legs[]) — realizable_edge_pp ≤ 0 means the overround exists only at last-trade, not in the book; do not trade it. For custom sizing use polymarket_fill_risk.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
eventNoSingle-event mode (use this if you know the specific Polymarket event): event slug like "fed-decision-may-2026" or "when-will-bitcoin-hit-150k". Full Polymarket URLs also accepted.
topicNoCross-event mode (use this if you want to scan related events across the platform): a topic or seed question like "Fed rate decision" or "Strait of Hormuz traffic returns to normal". Tool searches Polymarket for related events and checks monotonicity across them.

TDQS

A4.9/5.0
Behavior5/5

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

Annotations declare readOnly/openWorld/idempotent/non-destructive, and the description adds substantial behavioral detail beyond that: Jaccard similarity threshold (≥0.30), placeholder filter (>20% returns null), deviation >3pp for signal emission, and the fill-check against live CLOB depth. 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 long but well-structured with labeled sections (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK) and front-loaded purpose. Every sentence carries useful information, though slightly verbose; a light trim could improve readability without losing value.

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?

This is a complex tool with no output schema, and the description compensates by outlining response fields (opportunities[], partition_check), fill-check semantics, and edge cases like skipped_low_similarity and placeholders_filtered. It gives an agent everything needed to select 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 already has 100% coverage, but the description enriches both parameters with concrete examples ('fed-decision-may-2026'), mode semantics, and cross-event behavior. It clarifies that calling with no args triggers the trending scan, which is not in the schema.

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 a specific verb+resource: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It clearly distinguishes from siblings by naming the methodology and explicitly pointing to polymarket_fill_risk for custom sizing, leaving no ambiguity about its unique role.

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 when-to-use guidance across three modes: no args for trending_scan, `event` for per-event partition checks, and `topic` for cross-event scans. It also names the alternative tool for custom sizing and warns against trading when realizable_edge_pp ≤ 0.

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

All Fmi 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

A3.9/5.0
Disambiguation4/5

Most tools are well-differentiated, with detailed descriptions clarifying their distinct purposes. However, there is some overlap between the multiple 'ask' tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, suggest_questions) and between weather tools (forecast, latest_observations, recent_observations, warnings), which could cause confusion. Overall, ambiguity is low.

Naming Consistency5/5

All 34 tool names follow a consistent lowercase_with_underscores (snake_case) pattern. Names are descriptive and predictable, such as 'ask_pipeworx', 'entity_profile', 'polymarket_arbitrage', etc. No mixing of conventions like camelCase or inconsistent verb styles.

Tool Count3/5

The server has 34 tools, which is on the higher side given its broad scope covering weather, company research, prediction markets, and general data queries. While not excessive, it could be split into more focused servers for clarity. The count feels a bit heavy but still manageable.

Completeness4/5

The tool set covers a wide range of data domains (weather, company financials, prediction markets, news, memory, subscriptions) with reasonable completeness. Minor gaps exist, such as limited weather coverage (Finland only) and no direct support for non-company entities or unofficial data sources, but core workflows are well-supported.

Resources