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by io.github.pipeworx-io

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

A5/5.0
Behavior5/5

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

Annotations already declare the tool readOnly and idempotent, and the description adds substantial behavioral context beyond that: compatibility codes, skipped-pair counters, unclassified-leg exclusion, temporal alignment, and flags carried per spread entry. It also discloses the important quirk that both compatibility_warning and compatibility_codes can be non-empty even when matched_pairs > 0.

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

Conciseness5/5

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

The description is dense but well-structured with labeled sections (TWO MODES, RESPONSE, SAFETY FIELDS), and every sentence carries operational meaning. The warning at the end is not filler; it sets accurate expectations about real-world usability. Given the tool's complexity and the absence of an output schema, the length is 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 fully carries the burden of explaining return values, and it does so thoroughly: leg-by-leg prices, top_spreads_pp, compatibility fields, skipped_unclassified, low_confidence_pairs, temporal_alignment, and skip counters. An agent has enough to invoke the tool and interpret its result across all modes.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, yet the description still adds real value: it explains the relationship between the `topic` parameter and the two explicit override parameters, clarifies what 'overrides the topic-mapped side' means, and lists the exact shortcut values. This goes well beyond the baseline set by the schema 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 opens with a specific verb-resource pair: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It clearly distinguishes itself from same-venue tools like polymarket_arbitrage and polymarket_edges by emphasizing the cross-venue delta, and it names its two operating modes explicitly.

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?

The description gives explicit when-to-use guidance for both modes: use `topic` for 10 pre-mapped shortcuts and use explicit ticker/slug for custom pairings. It also warns that pre-mapped topics are often not tradeable and that compatibility warnings must be read even when pairs are returned, which is strong usage guidance.

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

A4.3/5.0
Disambiguation5/5

Each tool has a clearly defined, distinct purpose. Even closely related tools like ask_pipeworx and ask_pipeworx_grounded are differentiated by use case (casual vs. high-stakes), and the prediction market tools each cover a specific function (research, edge detection, arbitrage, fill risk, tracking, cross-venue spreads). Detailed descriptions eliminate ambiguity.

Naming Consistency4/5

Tool names predominantly follow a verb_noun pattern (e.g., ask_pipeworx, compare_entities, resolve_entity), but a few use noun_noun or adjective_noun forms (e.g., entity_profile, recent_alerts). The naming is generally predictable and readable, with minor deviations from a strict pattern.

Tool Count3/5

The server offers 32 tools, which is above the typical 3-15 range for a well-scoped server. However, the vast domain (financials, prediction markets, news, memory, subscriptions, etc.) justifies the count. It is on the heavy side but still manageable with clear organization.

Completeness5/5

The tool surface is remarkably complete for the apparent domain: exploration (discover_tools, suggest_questions), identifier resolution (resolve_entity), data retrieval (ask_pipeworx, deep_research, entity_profile, compare_entities, recent_changes, validate_claim), prediction market analysis (full suite), memory (remember/recall/forget), subscriptions (subscribe/unsubscribe/list/recent_alerts), and extras (ENS, dependency scan, AI visibility). No obvious gaps exist.

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