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Polymarket–Kalshi Spread

polymarket_kalshi_spread
Read-onlyIdempotent

Cross-venue spread between Kalshi and Polymarket for the same resolving question. The two venues sometimes price the same outcome 2-25pp apart because their participant pools differ — when the bet shapes are equivalent that delta is a real signal, when they aren't the tool says so. TWO MODES: (1) topic — 10 pre-mapped macro shortcuts ("fed", "btc", "cpi", "gdp", "sp500", "recession", "next_pope", "next_uk_pm", "next_israel_pm", "2028_president") auto-fetch the matching event on each venue. (2) explicit kalshi_event_ticker + polymarket_event_slug for custom pairings. RESPONSE: each venue's leg-by-leg prices (raw probability 0-1) plus matched spread[].top_spreads_pp (Kalshi − Polymarket) where the same outcome shows up on both sides. SAFETY FIELDS: compatibility_warning is a sentence and compatibility_codes[] the machine-readable form; BOTH can be non-empty on returned pairs, so read them even when matched_pairs>0. Codes: event_subject_mismatch (the two event titles share no subject words — probably not the same question), temporal_mismatch (they resolve in different months), non_equivalent_bet_shapes, no_candidate_pairs, unclassified_legs_excluded, premapped_pairing_unverified (always set in topic mode when pairs are returned: the legs were matched by keyword and word overlap, not a shared resolution source). Each entry in top_spreads_pp carries its own flags[]. A leg whose metric_type or match_subtype is "unknown" is NEVER paired — those comparisons land in spread.skipped_unclassified and, when the wording lined up, in spread.low_confidence_pairs[] for inspection only. temporal_alignment{polymarket_month,kalshi_month,aligned} tells you whether the two events resolve in the same calendar period. skipped_cross_type / skipped_cross_subtype counters expose how many leg-pair comparisons were dropped (cross-type = metric_type mismatch like MoM vs YoY; cross-subtype = inequality mismatch like cum_ge vs cum_le). Real cross-venue spreads are rarer than the macro-shortcut list suggests — most pre-mapped topics return compatibility_warning today; pre-mapped ≠ tradeable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicNoPre-mapped: fed | btc | cpi | gdp | sp500 | recession | next_pope | next_uk_pm | next_israel_pm | 2028_president
kalshi_event_tickerNoExplicit Kalshi event ticker, e.g. "KXFED-26OCT". Overrides the topic-mapped Kalshi side.
polymarket_event_slugNoExplicit Polymarket event slug, e.g. "fed-decision-in-june-825". Overrides the topic-mapped Polymarket side.

TDQS

A4.6/5.0
Behavior5/5

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

The description goes far beyond the readOnlyHint and idempotentHint annotations, disclosing response structure, compatibility codes, when legs are skipped, when low-confidence pairs are returned, and the meaning of temporal_alignment and skipped counters. It also warns that returned pairs may still carry compatibility warnings, giving the agent a clear picture of the tool's reliability.

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 long but every section earns its place given the tool's complexity. It is front-loaded with the core purpose, then logically proceeds through modes, response fields, safety fields, and caveats. Slightly dense, but well-organized and not padded.

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?

With no output schema, the description carries the full burden of explaining return values, and it does so thoroughly: leg prices, top_spreads_pp, compatibility_warning, compatibility_codes, skipped_unclassified, low_confidence_pairs, temporal_alignment, and skipped counters. An agent has enough detail to invoke the tool and interpret results 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?

Input schema already documents all three parameters with 100% description coverage. The description adds meaningful mode semantics: topic as a pre-mapped shortcut and the explicit ticker/slug as overrides. This supplements the schema without being redundant.

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 first sentence clearly defines the tool as calculating the cross-venue spread between Kalshi and Polymarket for the same resolving question. It distinguishes itself from sibling Polymarket tools by focusing on cross-venue comparison rather than single-venue arbitrage or edge tracking. The two-mode description further sharpens what the tool does.

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 explains when to use topic mode versus explicit ticker/slug mode, and warns heavily about the reliability of pre-mapped shortcuts ("pre-mapped ≠ tradeable"). It does not explicitly compare against sibling alternatives like polymarket_arbitrage, but gives enough contextual guidance for an agent to decide when this tool is appropriate.

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

ask_pipeworx and ask_pipeworx_beta are explicitly identical today, and ask_pipeworx_grounded, deep_research, and validate_claim all route to the same underlying sources with overlapping question-answering purposes. Entity-focused tools like entity_profile, compare_entities, recent_changes, and resolve_entity also have fuzzy boundaries that make selection error-prone.

Naming Consistency2/5

Names mix conventions: verb_noun (list_subscriptions, search_articles, generate_llms_txt), bare verbs (remember, recall, forget), noun phrases (polymarket_arbitrage, pipeworx_trending, entity_profile), and an ask_* family with beta/grounded variants. There is no consistent verb or noun pattern across the set.

Tool Count2/5

With 35 tools, the surface is well above the 15-tool threshold for a focused server, and most tools are unrelated to the NYT domain implied by the server name. The breadth reflects a broad data-platform grab bag rather than a scoped, intentional tool set.

Completeness4/5

The query side is unusually complete: single-lookup, grounded lookup, deep research, claim validation, entity resolution, comparison, profile, change-feed, discovery, memory, and subscription lifecycle tools are all present. Minor gaps remain, such as no direct NYT article fetch by URL and no update path for stored memories, but agents can work around them.