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Neso

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

A4.9/5.0
Behavior5/5

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

Annotations indicate read-only, idempotent, open-world. Description adds critical behavioral context: semantic anchor for cross-event pairs (Jaccard similarity ≥0.30), partition filter for placeholder slugs, fill check against CLOB depth, and response structure. 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?

The description is thorough and well-structured with sections, but slightly verbose. Front-loaded with main purpose, every sentence adds value, but could be slightly more concise.

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 no output schema, the description fully explains response fields (opportunities[], partition_check{}, fill check results) and even references polymarket_fill_risk for custom sizing. Provides enough context for an AI to use the tool 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?

Both parameters (event, topic) have schema descriptions, and the high-quality description adds examples of valid inputs (e.g., 'fed-decision-may-2026', 'Strait of Hormuz traffic returns to normal') and notes that full URLs are accepted for event. Coverage is 100%.

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: finding arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes between three modes (trending scan, event mode, topic mode) and differentiates from sibling tools like polymarket_edges and 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 Guidelines5/5

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

Explicit instructions for each mode: 'Call with NO args for a trending_scan', 'pass event for the strongest per-event partition_check', 'or topic for a themed cross-event scan'. Recommends event for specific markets and topic for cross-event scanning. Also 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.

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TDQS

A3.9/5.0
Disambiguation2/5

The ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded trio is genuinely confusing — beta is explicitly identical to stable, and grounded differs only in output format, so agents will struggle to pick the right one. The six Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread, bet_research) also blur together despite distinct purposes, and ai_visibility_check vs scan_competitor_ai_presence overlap heavily.

Naming Consistency4/5

Naming is largely consistent: snake_case throughout, with a strong verb-first pattern (list_subscriptions, resolve_entity, validate_claim, compare_entities, discover_tools) and clear domain prefixes for clusters (neso_, pipeworx_, polymarket_). The only inconsistency is the ask_pipeworx family, which differentiates by bare suffix (_beta, _grounded) rather than a descriptive verb.

Tool Count3/5

35 tools is on the heavy side, but the server is a broad multi-domain data platform (SEC, FDA, FRED, NESO, Polymarket, npm, memory, subscriptions, discovery) where the count is arguably justified. Several tools could be consolidated — the three ask_pipeworx variants are redundant, and scan_competitor_ai_presence largely wraps ai_visibility_check — which would tighten the surface meaningfully.

Completeness4/5

Coverage is strong across the stated domains: lookups, deep research, entity resolution, claim verification, comparisons, memory lifecycle (remember/recall/forget), subscription lifecycle (subscribe/unsubscribe/list/recent_alerts), discovery (discover_tools, suggest_questions), feedback, and trending. Minor gaps exist (no direct document-fetch tool, scan_dependency is npm-only in v1) but nothing that creates a dead end.

Resources