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Nihr

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

A4.7/5.0
Behavior5/5

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

Annotations already mark the tool as read-only, open-world, idempotent, and non-destructive. The description adds significant behavioral context: describes the response structure (opportunities[], partition_check), explains fill check logic (theoretical vs realizable edge), and warns against trading when realizable_edge_pp <= 0. No contradictions.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

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

Description is somewhat lengthy but well-structured with clear sections (SEMANTIC ANCHOR, PARTITION FILTER, FILL CHECK). Front-loads main purpose and usage modes. Some details like 'skipped_low_similarity surfaces the rejected pair count' could be moved to parameter descriptions, but overall not wasteful. Scores 3 for moderate conciseness.

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 has 2 optional parameters, no output schema, and high complexity, the description covers purpose, usage modes, behavioral nuances, and response structure comprehensively. Includes important constraints like Jaccard similarity threshold and placeholder filter. Adequate for an AI agent to correctly select and invoke the tool.

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 has 100% coverage with descriptions for both event and topic. The description adds value beyond schema by explaining the semantic difference, giving example inputs, and describing behavioral differences between modes (e.g., cross-event catches date-axis patterns single-event misses). Schema already adequate, so baseline 3; description adds enough extra context to warrant 4.

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?

Clearly states 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' Distinguishes from sibling tools like polymarket_edges, polymarket_edge_tracker, and polymarket_fill_risk by specifying unique methodology.

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?

Explicitly describes three usage modes: no args for trending_scan, event for single-event partition checks, topic for cross-event scanning. Provides specific guidance on when to use each, e.g., 'event (recommended for a specific market)' and explains cross-event mode catches patterns single-event misses. Also advises using 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

A3.7/5.0
Disambiguation2/5

The set contains several clusters of near-overlapping tools: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, ask_pipeworx/deep_research/validate_claim all handle natural-language queries, and bet_research/polymarket_edges/polymarket_arbitrage scan the same prediction-market space. The descriptions are detailed, but that does not remove the boundary confusion.

Naming Consistency4/5

Almost all tools use lowercase snake_case with recognizable patterns such as verb_noun or prefix_domain (nihr_, polymarket_, pipeworx_). There are minor deviations like ask_pipeworx_beta vs ask_pipeworx_grounded and mixed noun/verb phrasing, but the naming is predictable overall.

Tool Count2/5

36 tools is beyond the typical well-scoped server size, and the set reads as several products bundled together: NIHR grants, Pipeworx data research, prediction markets, memory, subscriptions, and standalone utilities like generate_llms_txt or scan_dependency. Even for a broad data platform this is too many to navigate coherently, and it is a severe mismatch for a server named 'Nihr'.

Completeness3/5

Within its subdomains the set covers core workflows: query (ask/deep_research/validate), entity resolution/profile/comparison, NIHR grant lookup by several dimensions, prediction-market analysis through fill-risk, and memory/subscription lifecycles. But it is a collection of partial products rather than one coherent domain, and some outputs such as pipeworx:// citations or detected arbitrage opportunities lack an obvious in-set tool to consume them further.

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