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Malwarebazaar

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 indicate readOnly, openWorld, idempotent, non-destructive. The description adds extensive behavioral context: algorithmic details (monotonicity, partition checks, Jaccard similarity, placeholder filter, fill check against CLOB depth), response structure, and trading advisories (e.g., realizable_edge_pp ≤ 0 means don't trade). 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 front-loaded with the core purpose and mode guidance. It is dense with information but well-organized. Some algorithmic details could be streamlined, but it remains efficient given the tool's complexity.

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?

Despite no output schema, the description thoroughly explains the response structure (opportunities array with fields, partition_check, fill_check). It covers edge cases (placeholder filter, Jaccard similarity, CLOB depth check). For a complex arbitrage tool, the description is remarkably complete.

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 100% of parameters with descriptions, but the description adds significant value: explains when to use each parameter (event for specific market, topic for cross-event scanning), provides examples of slugs and seed questions, and details behavioral differences between modes.

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 on Polymarket via monotonicity violations and partition-sum checks. It distinguishes three modes (no args, event, topic) with specific use cases, making the purpose unambiguous.

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?

The description explicitly advises when to use each parameter: 'Call with NO args for a trending_scan', 'event (recommended for a specific market)', 'topic (for cross-event scanning)'. It explains cross-event mode catches patterns that single-event misses. However, it does not explicitly contrast with sibling tools like polymarket_edges, though it references polymarket_fill_risk for custom sizing.

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

A3.5/5.0
Disambiguation2/5

Several tools are near-duplicates: ask_pipeworx_beta is described as currently identical to ask_pipeworx, and ask_pipeworx_grounded is the same router with extra verification. Malware sample searches also overlap across search_family, search_tag, search_signature, and recent_samples, while ai_visibility_check is wrapped by scan_competitor_ai_presence.

Naming Consistency3/5

The set mostly uses snake_case and verb-first names like get_sample_info and validate_claim, which is helpful. However, conventions are mixed across ask_pipeworx*, polymarket_*, pipeworx_*, search_*, and noun-style names like recent_samples and entity_profile, so there is no single predictable pattern.

Tool Count2/5

36 tools is already over the comfortable range, but the bigger issue is that the server is named Malwarebazaar while only about five tools actually deal with malware. The remaining tools belong to an unrelated data-research, prediction-market, memory, and subscription platform, making the count inappropriate for the server's apparent purpose.

Completeness2/5

For the MalwareBazaar domain, the set covers metadata lookups and filtered sample searches but lacks sample submission, retrieval, or deeper analysis workflow. The rest of the tool surface targets unrelated domains, so there is no coherent, complete lifecycle for either malware intelligence or the broader feature set.

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