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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 — BOTH modes run the identical token-overlap matcher, so the same disclosures apply to both. 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), temporal_alignment_unknown (the resolution month could not be parsed on one or both sides — NOT the same as confirmed-aligned; check each event's close/strike date yourself), non_equivalent_bet_shapes, no_candidate_pairs, unclassified_legs_excluded, pairing_unverified (set in EITHER mode whenever 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[] (temporal_mismatch, temporal_alignment_unknown, event_subject_mismatch, low_token_overlap). 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, in EITHER mode; null means it could not be computed (see temporal_alignment_unknown), not that the two sides align. spread.fees_note is a standing disclosure: Kalshi charges per-contract trading fees, Polymarket does not, and this tool does not model Kalshi's fee schedule — every spread_pp is gross, not a net tradeable edge. 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 annotations by explaining exactly what happens with unclassified legs ('A leg whose metric_type or match_subtype is "unknown" is NEVER paired'), the semantics of null temporal_alignment ('null means it could not be computed... not that the two sides align'), and the gross-vs-net spread disclosure: 'this tool does not model Kalshi's fee schedule — every spread_pp is gross, not a net tradeable edge.' It also explains skipped-cross counters and safety fields, giving agents complete behavioral expectations. There is no contradiction with readOnlyHint=true; the tool only reads and analyzes data.

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 substantially long but highly structured with labeled sections (TWO MODES, RESPONSE, SAFETY FIELDS, Codes, temporal_alignment, fees_note, skipped_cross_type/subtype). The opening sentence front-loads the core purpose, and all major topics are covered in logical order. While every sentence adds real value for such a complex tool, it is not lean; the repeated emphasis on compatibility warnings could have been condensed. For a tool with no output schema and many edge-case fields, this length is justified, earning a 4 rather than a 3.

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 present, the description fully specifies the return shape: 'each venue's leg-by-leg prices (raw probability 0-1) plus matched spread[].top_spreads_pp', as well as safety fields, compatibility_codes[] semantics, temporal_alignment behavior, fees_note, and skipped counters. It also explains the meanings of all compatibility codes and the per-entry flags[] in top_spreads_pp. For a tool with this level of conditional logic and no JSON schema to fall back on, the description is remarkably complete, leaving almost no ambiguity about what the agent will receive or how to interpret it.

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?

The schema already describes the three parameters with 100% coverage, listing the topic values and explicit overrides. The description adds meaning beyond the schema by explaining that both modes 'run the identical token-overlap matcher' and that explicit parameters 'override the topic-mapped side' of the pairing. It also clarifies the response naming (kalshi_event_ticker vs polymarket_event_slug) in the context of the two modes, which the schema alone does not convey. This is solid enrichment, though not an exhaustive re-explanation of all parameters.

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 opens with a precise resource: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It clearly states the tool computes a cross-venue spread and immediately qualifies the signal with the participant-pool difference and equivalent bet-shape condition. The name and sibling list (e.g., polymarket_arbitrage, polymarket_edges) already separate it from other venues, but the description reinforces this by naming both specific platforms and the concept of 'identical token-overlap matcher'.

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 lays out TWO MODES and how to choose between them: 'topic' shortcuts for 10 pre-mapped macros, or explicit kalshi_event_ticker + polymarket_event_slug for custom pairings. It also gives important usage cautions: 'pre-mapped ≠ tradeable' and 'most pre-mapped topics return compatibility_warning today.' However, it does not explicitly name sibling tools as alternatives or provide a 'when NOT to use' exclusion, so it stops short of a 5.

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

B3.3/5.0
Disambiguation2/5

Many tools have overlapping purposes, e.g., multiple ask_pipeworx variants and several Polymarket analysis tools. The presence of meta-tools like discover_tools and suggest_questions adds confusion. Distinguishing between tools like entity_profile, compare_entities, and recent_changes requires careful reading of descriptions.

Naming Consistency2/5

Naming conventions are mixed: some use snake_case (ai_visibility_check, ask_pipeworx), others use underscores (compare_entities, deep_research). Prefixes like pipeworx_ and polymarket_ are inconsistently applied, and there is no clear verb_noun pattern across the set.

Tool Count2/5

With 32 tools, the server is heavily over-scoped for its name 'Yc Rejection'. Only one tool directly relates to that domain. The rest constitute a full data platform, making the count far too high for the implied narrow purpose.

Completeness1/5

For a server named 'Yc Rejection', the tool set is severely incomplete: only one tool generates rejection text. There are no tools for application management, review, or related tasks. The actual completeness of the underlying platform is irrelevant given the misleading name.