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Setlist Fm

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

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds substantial behavioral context: fill check against live CLOB depth, partition filter dropping placeholder slugs, semantic anchor requiring Jaccard similarity >=0.30, and partition check with placeholder fraction threshold. These details go beyond annotations and are neither contradictory nor redundant.

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

Conciseness3/5

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

The description is verbose (over 500 words) with multiple paragraphs covering technical details like monotonicity, Jaccard similarity, and fill check. While front-loaded with the core purpose, it could be more concise by grouping related details. However, the structure flows logically from introduction to modes to internal checks to response format.

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?

Given no output schema and two parameters, the description is highly complete. It explains input modes, internal logic (monotonicity, partition sum, semantic anchor, placeholder filter), response structure (opportunities array, partition check, fill check), and even a pointer to polymarket_fill_risk for custom sizing. An AI agent would have enough context to select and invoke the tool correctly.

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 coverage is 100% with descriptions for both parameters. The description adds significant meaning: explains event mode uses slug/URL and walks child markets, topic mode searches related events and flattens markets. It provides concrete examples ("fed-decision-may-2026", "Fed rate decision") and explains behavioral differences. This adds value beyond the schema, but some parameter semantics are already covered in schema 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 clearly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes three modes (no args, event, topic) with specific verbs and resources, setting it apart from sibling tools like polymarket_edges or polymarket_fill_risk. The verb 'find' and resource 'arbitrage opportunities on Polymarket' are specific and 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 provides explicit when-to-use guidance: call with no args for trending scan, pass event for single-event partition check, pass topic for cross-event scanning. It recommends event mode for specific markets and warns about fill check conditions. It also mentions when not to trade (realizable_edge_pp <= 0). Although sibling alternatives like polymarket_fill_risk are mentioned for custom sizing, the description does not contrast with siblings like polymarket_edges, but the mode guidance is comprehensive enough.

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

A3.6/5.0
Disambiguation2/5

The tool set mixes two completely different domains: setlist.fm (12 tools) and Pipeworx data services (30+ tools). An agent cannot easily distinguish which tools belong to the server's primary purpose, leading to confusion and misselection.

Naming Consistency2/5

Setlist.fm tools use consistent verb_noun patterns (artist, artist_search, artist_setlists), but the majority of tools follow no unified convention: some use snake_case (ai_visibility_check), others use mixed case (ask_pipeworx), creating an inconsistent naming landscape.

Tool Count2/5

43 tools is excessive for a setlist.fm API. Only about 12 are relevant; the remaining 31 are unrelated and bloat the tool surface, making it hard to navigate and maintain.

Completeness4/5

For the setlist.fm domain, the tools cover search, retrieval, and user data comprehensively (artists, setlists, venues, cities, countries, users). Minor gaps exist (e.g., no update/delete operations), but core workflows are supported.

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