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Food Feeds

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

A4.3/5.0
Behavior5/5

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

The description goes far beyond the annotations by explaining the matching algorithm, compatibility codes, null semantics for temporal_alignment, limitations of gross spreads, skipped counters, and unclassified leg handling. It also explicitly warns that pairing is keyword-based and not verified by a shared resolution source, which is exactly the kind of behavioral caveat an agent needs.

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 it is organized with labeled sections and every section contributes safety-relevant or operational information that is not available elsewhere. Some repetition and heavy parentheticals could be tightened, but the length is largely justified given there is no output schema and the tool has many edge-case behaviors.

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 tool with no output schema and substantial behavioral complexity, this description is remarkably complete. It documents response fields, compatibility code semantics, skipped leg categories, fee caveats, spread sign convention, and what null means in temporal_alignment. An agent has enough context to call the tool and interpret its 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?

Schema coverage is 100%, so the baseline is 3, and the description adds real meaning on top: it explains that topic is a pre-mapped shortcut, that explicit ticker and slug override the mapped side, and that both modes run the same matcher. It does not redefine the schema but gives the parameters practical semantics and relationships.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific resource and objective: cross-venue spread between Kalshi and Polymarket for the same resolving question. It clearly explains the two modes and what the tool returns, but it does not explicitly distinguish itself from sibling tools such as polymarket_arbitrage, so the purpose is clear without being explicitly differentiated from close alternatives.

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 gives unusually clear guidance on how to choose between topic shortcuts and explicit pairings, and it warns that pre-mapped topics are not inherently tradeable. It does not name alternatives like polymarket_arbitrage or polymarket_edges or state when this tool should be preferred over them, but the situational context for invoking it and interpreting results is strong.

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

B3.4/5.0
Disambiguation2/5

Many tools have overlapping functionality (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the set includes both food-specific feeds and general data tools without clear separation. Distinguishing between them, especially for an agent, would be difficult.

Naming Consistency2/5

Tool names follow no consistent pattern: some are snake_case (list_feeds, read_feed), others are lowercase with underscores (ai_visibility_check), and many are multi-word without clear structure (polymarket_arbitrage, scan_dependency). This inconsistency makes it hard to predict tool names.

Tool Count1/5

With 34 tools, the count is high, and most tools are unrelated to the server's stated purpose of 'Food Feeds'. The inclusion of general-purpose Pipeworx tools (e.g., deep_research, entity_profile, polymarket tools) makes the set bloated and unfocused.

Completeness2/5

For the food feeds domain, only three tools (list_feeds, read_feed, fetch_feed) are relevant, which is incomplete. The server lacks tools for searching, subscribing, or managing feeds. The presence of many unrelated tools does not compensate for this gap.