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Glama

Statbel Be

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. First observed

TDQS

A5/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, non-destructive. The description adds substantial behavioral context: it explains the fill check (comparing theoretical vs realizable edge at 1000 shares/leg), warns 'do not trade it' when realizable_edge_pp ≤ 0, mentions skipped_low_similarity and placeholder filters, and outlines the return structure. No contradiction 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?

The description is long but exceptionally structured: it uses labeled sections (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK) and prescriptive lines. It front-loads the core purpose and then layers behavior details. Every sentence adds value; no filler. The length is justified by the complexity of the tool and the lack of an output schema to convey return semantics.

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 there is no output schema, the description compensates by fully specifying the response: 'opportunities[] (gap_pp, suggested_trade, reasoning, monotonicity violation context)' and 'partition_check{...}'. It covers edge cases (thin legs, placeholder fraction, low similarity), explains the three operating modes, and mentions related tools for follow-up actions. This is a complete picture for an agent to use it 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?

While the input schema describes the two parameters (event, topic), the description enriches them significantly: it gives concrete examples of event slugs ('fed-decision-may-2026') and topic seed questions, explains what each mode does with the parameter, and clarifies the no-arg behavior that is not apparent from the schema. Schema coverage is 100% but the description goes far beyond.

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 specific verb+resource+method: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It clearly distinguishes from sibling tools by detailing the unique techniques and modes (trending_scan, event, topic). References to 'polymarket_fill_risk' for custom sizing further differentiate it.

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 instructions are provided for each mode: 'Call with NO args for a trending_scan', 'pass `event` for...', 'pass `topic` for...'. It also gives examples of inputs, explains when one mode is preferred over another (e.g., 'event (recommended for a specific market)'), and explicitly names an alternative tool for custom sizing: 'For custom sizing use polymarket_fill_risk.'

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

A3.7/5.0
Disambiguation2/5

The set contains several clusters of overlapping tools: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve broad data-query purposes, while the six Polymarket-related tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) heavily overlap in scope. The detailed descriptions help, but an agent would frequently struggle to choose the correct tool among near-synonyms.

Naming Consistency3/5

All names use snake_case, but the underlying pattern is inconsistent: list_*/get_* for Statbel, ask_* for query routers, noun-heavy names like entity_profile, bet_research, polymarket_edges, and recent_alerts, plus bare verbs like remember, recall, forget, subscribe, unsubscribe. It remains readable, but there is no single predictable verb_noun convention across the set.

Tool Count2/5

At 35 tools, the set exceeds the range where each tool clearly earns its place, and the scope is wildly broad: Belgian statistics, general data lookup, prediction markets, npm dependency checks, memory, subscriptions, llms.txt generation, and AI visibility. A server named 'Statbel Be' carries 31 tools unrelated to that name, which makes the count feel bloated and unfocused.

Completeness2/5

For the Statbel domain implied by the server name, the surface is severely incomplete: list_datasets, get_dataset, list_views, and get_view only return metadata — there is no tool to actually fetch the statistical data values. For the broader Pipeworx data-access domain, coverage is more complete, but the server's stated purpose is under-served and leaves core workflows at a dead end.

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