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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?

Annotations already declare read-only, idempotent, non-destructive behavior, and the description adds extensive operational caveats: compatibility_codes, pairing_unverified, gross-vs-net fees, skipped leg counters, and temporal_alignment semantics. It even tells the agent to read safety fields even when matched_pairs > 0, which is valuable beyond the 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 dense but well-structured with labeled sections like TWO MODES, RESPONSE, SAFETY FIELDS, and Codes. Some caveats are lengthy and partially repetitive, but every sentence carries safety-relevant or mode-selection information, so the length is mostly justified.

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 still covers all essential response components: leg prices, top_spreads_pp, compatibility_codes, temporal_alignment, fees_note, skipped counters, and per-entry flags. This is enough for an agent to invoke the tool and correctly interpret the results, including risk-relevant caveats.

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%, and the description adds meaningful context by explaining that topic is a pre-mapped shortcut while the explicit parameters override the topic-mapped side. It also clarifies that both invocation modes share the same token-overlap matcher, which goes beyond the bare schema property 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 it computes the cross-venue spread between Kalshi and Polymarket for the same resolving question. It explains what the delta means and distinguishes itself from ambiguity by describing two modes and where the tool explicitly calls out non-equivalent bet shapes.

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?

Explicitly documents TWO MODES: topic shortcuts versus explicit kalshi_event_ticker + polymarket_event_slug pairings, plus the fact that both modes run the same matcher. It also warns that pre-mapped topics are often not tradeable, but it does not explicitly point to sibling tools for when this tool should not be used.

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

A4.4/5.0
Disambiguation5/5

Each tool has a clear, distinct purpose. The various `ask_pipeworx*` variants are differentiated by mode (single vs grounded vs research). `get_nav_history` vs `latest_nav` serve different query granularities. Administrative tools like `remember`/`recall`/`forget` are clearly separate. No two tools overlap in functionality.

Naming Consistency4/5

Tool names follow a consistent snake_case convention and generally use `verb_noun` order (e.g., `ask_pipeworx`, `search_schemes`, `validate_claim`). A few exceptions like `entity_profile` (noun_verb) exist, but the pattern is mostly predictable.

Tool Count3/5

At 34 tools, the server is quite large, including many specialized tools (e.g., multiple Polymarket tools, administrative memory/subscription tools) that could arguably be split into separate servers. The number feels slightly excessive for a coherent, focused server.

Completeness5/5

The tool set covers an extraordinarily wide range of domains: company financials, SEC filings, FDA drugs, economic data, mutual funds, real estate, prediction markets, npm dependencies, AI visibility, and more. It also includes memory, subscription, and feedback mechanisms. There are no obvious gaps for the domains addressed.