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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 — 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

A4.7/5.0
Behavior5/5

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

Annotations already cover readOnly/openWorld/idempotent/no-destructive hints, and the description goes well beyond them: it documents safety fields (compatibility_warning, compatibility_codes[]), clarifies that temporal_alignment_unknown is not confirmed-aligned and null ≠ aligned, discloses that spreads are gross and do not model Kalshi fees, and explains skipped_cross_type/subtype counters. It also states explicitly that unclassified legs are never paired. No contradiction with 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 front-loaded with the core purpose and organized with CAPS-labeled sections for modes, response, safety fields, and caveats. It is long, but each sentence carries non-redundant operational detail; the density is justified by the tool's complexity, though a more compact summary of edge cases would improve readability.

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?

There is no output schema, but the description fully enumerates the response shape: leg-by-leg prices, spread[].top_spreads_pp, compatibility_warning, compatibility_codes[], per-entry flags[], skipped_unclassified, low_confidence_pairs[], temporal_alignment, fees_note, and skip counters. It also explains how to interpret null values and non-empty safety fields, so an agent has everything needed to invoke and parse the tool correctly.

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%, but the description adds meaningful semantics: explicit ticker/slug parameters 'Override the topic-mapped' side, both modes run the identical token-overlap matcher, and concrete example values (KXFED-26OCT, fed-decision-in-june-825) are provided. This is more than the schema's field descriptions alone.

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 opening sentence is a precise statement of function: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It names the exact resources and operation, and the cross-venue framing distinguishes it from Polymarket-only siblings like polymarket_edges or polymarket_arbitrage.

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 — topic shortcuts vs explicit kalshi_event_ticker + polymarket_event_slug — and explains when each applies. It also sets expectations with 'pre-mapped ≠ tradeable' and warns that most topics return compatibility warnings. No alternative sibling tool is named, but the mode-level usage guidance is clear and actionable.

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

A3.9/5.0
Disambiguation3/5

There are several clusters of tools with overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all do data-fetching/research with somewhat subtle differences. Polymarket tools also overlap (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, bet_research, polymarket_kalshi_spread). However, most tools have detailed descriptions that clarify their distinct roles, and the core data-lookup tools are differentiated by grounding level and scope.

Naming Consistency4/5

Most tools follow a consistent verb_noun or noun_verb pattern (e.g., search_registrants, list_foreign_principals, get_registrant_documents, subscribe, unsubscribe, remember, recall, forget, resolve_entity, validate_claim). Deviations include brand-name tools like ask_pipeworx, pipeworx_feedback, pipeworx_trending, and polymarket_kalshi_spread that mix conventions but are still readable and predictable within their domain.

Tool Count2/5

34 tools is heavy for a single MCP server, especially with multiple overlapping research entry points (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, suggest_questions). The broad data-router nature of the server explains the size, but it is still a large surface that would be better consolidated.

Completeness4/5

The server covers its visible domains well: data lookup, grounded verification, entity profiling, comparison, change tracking, subscription lifecycle, memory, and FARA-specific queries. Minor gaps exist (e.g., no direct tool for updating saved memory beyond forgetting/re-remembering, no tool to create custom alert types beyond the three supported categories), but the core workflows are complete.

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