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Census

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

A4.7/5.0
Behavior5/5

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

Description adds extensive behavioral context beyond annotations: explains semantic anchor (Jaccard similarity ≥0.30), partition filter (placeholder >20% returns null), fill check with book depth thresholds (realizable_edge ≤ 0 means do not trade), and specific thresholds like deviations >3pp. 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.

Conciseness4/5

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

Description is long but well-organized: starts with purpose, then explains each mode, then details about filters, response, and fill check. Every sentence adds value, though somewhat dense. Could be slightly more concise but still well-structured.

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 the complexity (multiple modes, fill check, partition check), the description is very complete. Output schema not provided but description mentions response structure (opportunities array, partition_check fields). References sibling tool polymarket_fill_risk for custom sizing.

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?

Schema covers both parameters with descriptions (100% coverage). Description adds additional context: event slug examples, topic seed question examples, and explains what each mode does. Goes beyond schema by explaining underlying logic.

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?

Description clearly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes between two modes (event and topic) and mentions trending_scan when no args. It also differentiates from sibling tools like polymarket_edges, polymarket_edge_tracker, 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 Guidelines4/5

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

Explicitly says when to use each argument: event recommended for specific market, topic for cross-event scanning. Also notes cross-event mode catches patterns single-event misses. Does not explicitly state when not to use, but provides clear 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

A3.8/5.0
Disambiguation2/5

Multiple tools appear to do the same thing: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all provide natural-language data lookup, with ask_pipeworx_beta explicitly identical to ask_pipeworx. The six Polymarket tools also have heavily overlapping boundaries, and the Census-specific tools overlap with each other and with ask_pipeworx's routing to Census data.

Naming Consistency3/5

All names are lowercase snake_case, which is consistent, but the pattern is a mix: some are verb_noun (discover_tools, generate_llms_txt, list_subscriptions) while many are noun_verb or bare noun phrases (entity_profile, pipeworx_feedback, polymarket_edges, census_acs). Suffixes like _beta, _grounded, and the scattered noun-first names prevent a uniform convention.

Tool Count2/5

36 tools is well into the too-heavy range for a coherent server. Even if the domain truly is broad data research and prediction markets, many of these tools are near-duplicates or serve the same purpose with minor variations, so the count feels inflated rather than scoped.

Completeness4/5

The broad data-research domain is well covered: single lookups (ask_pipeworx), grounded verification (ask_pipeworx_grounded, validate_claim), multi-source research (deep_research, entitity_profile, compare_entities, recent_changes), plus memory and subscription utilities. Minor gaps exist (e.g., no general data update/delete, but data is read-only; no direct Census variable explorer beyond census_available_datasets), but no critical dead ends appear.

Resources