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Valorant

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

A4.8/5.0
Behavior5/5

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

The description goes far beyond annotations (readOnly, idempotent, openWorld). It reveals internal algorithms (monotonicity violations, Jaccard similarity threshold, placeholder filtering), the fill check mechanism, and trade signals like 'BUY/SELL EVERY LEG'. It warns about conditions under which the tool returns null signals or untradeable results, providing critical behavioral context not available from annotations alone.

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 fairly long but every sentence serves a purpose, introducing modes, algorithms, filters, and output format in a logical order. It is not tautological and avoids redundancy. Some minor reorganization could improve readability, but overall it is well-structured and front-loaded with the core purpose.

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 multiple modes and a detailed trade recommendation pipeline, the description covers all necessary aspects: how to invoke each mode, what the response contains (including field names like gap_pp, suggested_trade), fill check behavior, and edge cases like placeholder filtering and low similarity. Despite lacking an output schema, the prose description of the response structure is sufficient for an agent to consume the output correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds significant value by explaining what constitutes a valid event slug ('fed-decision-may-2026') and topic seed question, and that full Polymarket URLs are accepted. It also provides usage examples, which enriches the parameter semantics beyond the schema 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 three distinct call modes (no args, event, topic) and uses specific verbs like 'find', 'scan', 'pass', making it easy for an agent to understand what the tool does and how it differs from siblings like polymarket_edges or 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 Guidelines5/5

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

Explicit guidance is provided for each mode: 'Call with NO args for a trending_scan...', 'event (recommended for a specific market)...', 'topic (for cross-event scanning)...'. It also explains when to use event vs topic, and advises against trading when fill check shows unrealizable edge. The mention of 'For custom sizing use polymarket_fill_risk' directly points to an alternative sibling tool.

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 have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly described as currently identical, and ask_pipeworx_grounded shares the same router. ai_visibility_check is internally wrapped by scan_competitor_ai_presence, discover_tools and suggest_questions both claim 'use this FIRST' as onboarding meta-tools, and entity_profile/compare_entities/recent_changes pull overlapping EDGAR/news/patents data. The detailed descriptions help, but the redundancy is structural, not just cosmetic.

Naming Consistency3/5

All names are snake_case and several families are internally consistent (ask_pipeworx_*, polymarket_*, list_*), but the overall set mixes bare verbs (remember, recall, forget, subscribe), verb_noun (get_agent, compare_entities), noun_noun (entity_profile, bet_research, recent_changes), and adjective/compound forms (deep_research, generate_llms_txt, ai_visibility_check) with no dominant convention. Readable, but clearly heterogeneous.

Tool Count2/5

35 tools is above the 25+ 'too many' threshold, and the count is wildly mismatched to the server's stated identity: a server named 'Valorant' has only 4 game-related tools while the other 31 form a sprawling data-research/prediction-market/utility toolkit. Even considered on its own terms, the set includes several redundant meta-tools and unrelated subsystems (npm scanning, llms.txt generation, memory) that feel bolted on.

Completeness3/5

The dominant data-research domain is well covered: discovery, single and grounded queries, deep research, entity resolution, profiles, comparisons, change feeds, claim validation, and subscription monitoring form a mostly complete surface. However, the server's namesake domain is severely shallow — the Valorant tools only expose static reference data with no match/player/esports coverage — and the prediction-market side lacks obvious write-side or position-management operations.

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