Skip to main content
Glama

Data La

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, destructiveHint=false. Description adds significant behavioral details: monotonicity checks, partition sum validation, fill check (realizable vs theoretical edge), Jaccard similarity for cross-event, placeholder filtering. No contradictions.

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?

Front-loaded with main purpose, but description is dense and includes detailed algorithmic explanations (Jaccard threshold, placeholders, fill check). While informative, slightly verbose for a tool description. Structured with clear sections per mode.

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?

No output schema, but description fully documents response format: opportunities[] with fields (gap_pp, suggested_trade, reasoning, monotonicity context) and partition_check for event mode. Covers edge cases (placeholder filtering, fill check, low similarity). Annotations provide safety context, making the description complete.

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 covers both parameters with descriptions (100% coverage). Description adds richer semantics: explains event mode walks child markets and checks date-axis/threshold-axis ordering; topic mode searches related events and runs comparator on union. Goes beyond schema by explaining algorithmic behavior.

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?

Clearly states tool finds arbitrage opportunities on Polymarket via monotonicity violations and partition-sum checks. Distinguishes three modes (no args, event, topic) with specific use cases, differentiating from sibling tools like polymarket_edges or polymarket_fill_risk.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells when to use each argument: no args for trending scan, event for specific market, topic for cross-event scanning. Gives recommendations (event recommended for specific market) and explains why cross-event catches missed patterns. Does not directly compare to sibling tools but provides strong context for selection.

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

All Data La 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
Disambiguation3/5

Many tools have closely related or overlapping purposes: ask_pipeworx, ask_pipeworx_beta (currently identical), and ask_pipeworx_grounded all route to the same underlying toolset, while discover_tools and suggest_questions both help agents discover capabilities. The polymarket_* family also has several opportunity-scanning tools with subtle differences, though detailed descriptions help clarify.

Naming Consistency3/5

Names are mostly snake_case but follow mixed patterns: verb-first (ask_pipeworx, validate_claim), noun-first (entity_profile, bet_research, la_recent), and bare verbs (remember, forget, unsubscribe). The prefix groups (la_, pipeworx_, polymarket_) show some consistency, but there is no uniform verb_noun convention.

Tool Count2/5

34 tools is well into the 'too many' range for a single server. The surface bundles several distinct domains—structured data querying, prediction markets, LA open data, memory, subscriptions, and npm scanning—making it feel like a kitchen sink rather than a focused toolset.

Completeness4/5

Within each bundled sub-domain, coverage is strong: query/grounded/research/entity-profile/compare/validate covers data workflows; polymarket tools include research, edge scan, arbitrage, fill-risk, and cross-venue spread; LA data has search/query/recent; memory and subscription lifecycles are fully CRUD. Minor gaps exist (e.g., no way to browse LA dataset attributes beyond search), but no major dead ends.

Resources