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Pride

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

A5/5.0
Behavior5/5

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, but the description adds substantial behavioral context: the partition filter dropping placeholder slugs, the semantic anchor requiring ≥0.30 Jaccard similarity, the fill check that prices theoretical vs realizable edge, and the condition that realizable_edge_pp ≤ 0 means do not trade. This far exceeds the annotation baseline.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Though lengthy, the description is densely packed with unique, actionable information. It is front-loaded with the core purpose, then organized into labeled sections (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK). Every sentence contributes to correct tool usage, with no filler or repetition of schema data.

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?

For a tool with two modes, complex filtering logic, and no output schema, the description is remarkably complete. It details the response structure (opportunities[], partition_check), explains edge cases (thin_legs, placeholders_filtered), and provides decision rules (realizable_edge_pp ≤ 0). The description alone is sufficient for an agent to invoke and interpret results 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% with descriptions for both event and topic, and the tool description enriches both: it provides concrete examples (event slugs like 'fed-decision-may-2026'), explains the behavior of each mode (partition_check vs cross-event comparator), and clarifies that no args triggers trending_scan. The description adds significant meaning beyond 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 clear, specific statement: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' This includes the verb (find), resource (Polymarket arbitrage), and method, distinguishing it from siblings like polymarket_edges. It further elaborates on two distinct modes (event and topic), making its scope unambiguous.

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 gives explicit when-to-use guidance: 'Call with NO args for a trending_scan', 'event (recommended for a specific market)', and 'topic (for cross-event scanning)'. It also names an alternative tool, polymarket_fill_risk, for custom sizing, and explains when not to trade via fill check. This is exemplary usage direction.

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

A3.9/5.0
Disambiguation2/5

Several tools are near-identical in purpose: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route the same style of question, and the company-research tools (entity_profile, compare_entities, recent_changes) and Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research) have overlapping triggers. The long descriptions help, but an agent could easily select the wrong one.

Naming Consistency3/5

All names are snake_case and mostly readable, but conventions are mixed: some are verb-led (ask_pipeworx, resolve_entity, scan_dependency), some are noun-led (entity_profile, pipeworx_feedback, polymarket_arbitrage), and some are adjective-noun phrases (recent_changes, recent_alerts). The polymarket_* and ask_pipeworx_* families are internally consistent, but the overall set has no unifying pattern.

Tool Count2/5

33 tools is high and the server bundles several unrelated domains: Pipeworx data research, prediction markets, PRIDE proteomics, memory, subscriptions, and AI-visibility checks. Each cluster is individually useful, but the aggregate surface feels over-stuffed rather than well-scoped for a single MCP server.

Completeness4/5

The main clusters are well covered: query/grounded/deep research, entity resolution/profile/compare/validate, memory CRUD, subscription lifecycle, and a rich Polymarket analytics toolkit. Minor gaps exist—PRIDE is limited to metadata search/get and there is no trade execution for prediction markets—but most workflows can be completed without dead ends.

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