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Glama

Hathitrust

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 already declare read-only, idempotent, non-destructive. Description adds critical behavioral details: the fill check against live CLOB depth, semantic anchor with Jaccard similarity, partition filter for placeholder slugs, and response 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.

Conciseness4/5

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

The description is thorough but somewhat lengthy; however, it is well-structured with paragraphs and bolded key terms. Every sentence adds value, but for an AI agent a slightly more condensed version could improve parseability. Still very effective.

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 tool's complexity (multiple modes, fill check, semantic anchor, partition filter) and lack of output schema, the description covers all relevant aspects: input behavior, response fields, and usage caveats. It is complete and leaves no critical gaps.

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 coverage is 100%, but the description adds significant meaning: explains what event slug and topic string represent, provides examples, and clarifies how each parameter affects the scan (single-event vs. cross-event). This fully compensates and exceeds schema detail.

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 explicitly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks. It clearly distinguishes three modes (no args, event, topic) and what each does, leaving no ambiguity about purpose.

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?

Provides explicit guidance on when to use each mode: no args for trending scan, event for specific market with slug, topic for cross-event scan. Also warns when not to trade (if realizable_edge_pp ≤ 0) and references polymarket_fill_risk for custom sizing, effectively differentiating from siblings.

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 tools from multiple unrelated domains (HathiTrust, Pipeworx data platform, Polymarket, npm, etc.), making it unclear which tool to use for a given task. Overlap exists between ask_pipeworx and ask_pipeworx_grounded, and between several Polymarket tools, while the HathiTrust-specific tools are few and buried among many others, causing confusion.

Naming Consistency2/5

Naming conventions are inconsistent across the tool set. Some tools use verb_noun (check_full_view, lookup_by_identifier), others use noun_verb (entity_profile, deep_research), and some are single verbs (forget, remember, subscribe). There is no predictable pattern, making it harder for an agent to guess tool names.

Tool Count1/5

With 33 tools, the count is high, but only 3 (get_record, lookup_by_identifier, check_full_view) are relevant to the server's stated purpose (HathiTrust). The remaining 30 tools appear to be from unrelated domains (Pipeworx data platform, Polymarket, npm, etc.), making the tool count severely mismatched with the server name.

Completeness2/5

For the HathiTrust domain, the tool surface is severely incomplete: only three basic lookup tools with no search, browse, or management capabilities. The broader set includes tools for many other domains, but that does not compensate for the lack of coverage of the server's primary purpose.

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