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Macrostrat

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?

The description goes well beyond the annotations (readOnly, openWorld, idempotent). It discloses the fill-check logic (realizable vs theoretical edge, thin_legs), the semantic anchor threshold (≥0.30 Jaccard), the placeholder filter (>20% drop), and warns 'do not trade it' when realizable_edge_pp ≤ 0. These are important behavioral traits not inferable from annotations.

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?

The description is long but every sentence earns its place, covering modes, filters, response format, and fill check in a structured, front-loaded manner. It avoids redundancy and is organized logically—each section (SEMANTIC ANCHOR, PARTITION FILTER, RESPONSE, FILL CHECK) adds essential detail without fluff.

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 specifying the response format: '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}'. It also covers edge-case behaviors and fill-check nuances, making the tool's behavior fully transparent for an agent.

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?

Although the schema already covers both parameters (100% coverage), the description adds substantial value: concrete event slug examples ('fed-decision-may-2026'), acceptance of full URLs, seed question examples for topic, and explains what each mode does with parameters. This enriches the parameter semantics beyond the schema's concise descriptions.

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 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks', which is a specific verb, resource, and method. It clearly differentiates from sibling tools like polymarket_edges (edge detection) and polymarket_fill_risk (fill risk), while also detailing three distinct modes (trending_scan, event, topic) that define its scope.

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?

Explicit usage guidance is given: '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.' It even recommends which mode to use for a specific market and directs to sibling tool polymarket_fill_risk for custom sizing. This is clear when-to-use and alternative guidance.

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

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes, e.g., geology tools vs. Polymarket tools vs. memory tools. The main ambiguity is between ask_pipeworx and ask_pipeworx_grounded, but their descriptions clearly differentiate them (grounded vs. casual). Overall, an agent can reliably select the right tool.

Naming Consistency3/5

Names use snake_case consistently, but the structure varies: some are verb_noun (find_columns), some are noun_noun (entity_profile), some are single verbs (forget). This mix reduces predictability, though each name is still readable.

Tool Count3/5

With 29 tools, the server covers many domains (geology, finance, prediction markets, memory, subscriptions). This is a large surface for a server named 'Macrostrat', which implies a geology focus. The count feels bloated for a coherent set, though each tool individually seems justified.

Completeness4/5

The tool set is quite complete for its diverse sub-areas: geology has lookup tools, Pipeworx/Poly market has search, comparison, arbitrage, and memory/subscriptions have full CRUD. Minor gaps exist (e.g., no detailed geology unit edits), but overall coverage is strong.

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