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Spotify

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. Changed1 schema field changed
    • removedInput schema / anyOf
      Removed value: -[
-  {
-    "required": [
-      "event"
-    ]
-  },
-  {
-    "required": [
-      "topic"
-    ]
-  }
-]
  2. Changed3 schema fields changed
    • addedInput schema / anyOf
      Added value: +[
+  {
+    "required": [
+      "event"
+    ]
+  },
+  {
+    "required": [
+      "topic"
+    ]
+  }
+]
    • changedInput schema / properties / event / description
      Previous value: -"Single-event mode: Polymarket event slug (e.g. \"when-will-bitcoin-hit-150k\") or full URL."New value: +"Single-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."
    • changedInput schema / properties / topic / description
      Previous value: -"Cross-event mode: a topic or seed question. Tool searches Polymarket for related markets across separate events and checks monotonicity across them. E.g. \"Strait of Hormuz traffic returns to normal\"."New value: +"Cross-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."
  3. Added

TDQS

A5/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description discloses specific behaviors: the top ~200 markets scope, the >3pp deviation threshold, placeholder filtering with >20% threshold, the Jaccard similarity anchor (≥0.30), and the FILL CHECK logic that warns against trading when realizable_edge_pp ≤ 0. No contradictions with 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?

Though lengthy, the description is exceptionally well-structured with labeled sections (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK) and a clear front-loaded purpose. Every sentence adds meaningful information about modes, thresholds, or response handling, with no filler or redundancy.

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 complex tool with no output schema, the description provides comprehensive coverage: it explains all three invocation modes, response structure (opportunities[] with gap_pp, suggested_trade, etc.), edge-case handling (placeholder filtering, low similarity), and integration with fill check and other tools. The context signals (2 params, 100% schema coverage) are fully leveraged.

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?

While the schema already describes the parameters, the description adds rich semantics: accepted input formats (event slugs, full Polymarket URLs, seed questions), examples ('fed-decision-may-2026', 'Strait of Hormuz traffic returns to normal'), and the behavioral implications of each mode. This goes far beyond the schema's basic 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's purpose: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It distinguishes itself from sibling tools like polymarket_edges and polymarket_edge_tracker by specifying the unique methodology (monotonicity + partition-sum) and by referencing polymarket_fill_risk for sizing, showing clear differentiation.

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 guidance is provided for when to use each mode: 'Call with NO args for a trending_scan', 'event (recommended for a specific market)', and 'topic (for cross-event scanning)'. It also explains when not to trade via the FILL CHECK section and directs to polymarket_fill_risk for custom sizing, offering clear alternatives and context.

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

B3.1/5.0
Disambiguation1/5

The tool set is a chaotic mix of Spotify music tools and an extensive Pipeworx data querying system, with no clear separation. Tools like 'ask_pipeworx', 'ask_pipeworx_grounded', and 'deep_research' have overlapping querying purposes, while unrelated tools from prediction markets and company research further muddy the boundaries. An agent would struggle to distinguish which tool to use for a given task.

Naming Consistency2/5

Naming conventions are inconsistent: Spotify tools follow a verb_noun pattern (e.g., 'get_album', 'search'), while Pipeworx tools use descriptive phrases (e.g., 'ask_pipeworx', 'bet_research'). Additionally, some tools have vague names like 'process' or 'run' that could apply to anything. The lack of a unified naming scheme makes the set feel disjointed.

Tool Count2/5

With 36 tools, the count is high, but the server name 'Spotify' implies a focused music service. The majority of tools are unrelated to Spotify (e.g., Pipeworx queries, Polymarket bets, dependency scanning), making the tool count feel inflated and inappropriate for the stated domain. A more focused set would be far more coherent.

Completeness1/5

If this is a Spotify server, it is severely incomplete: it lacks playlist management, user actions, and recommendation features beyond top tracks. The inclusion of dozens of unrelated tools (financial data, prediction markets, package analysis) suggests the server has no clear purpose, leaving it neither complete for Spotify nor for any other single domain.

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