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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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.3/5.0
Behavior5/5

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

Even though annotations already mark this as read-only and idempotent, the description goes far beyond them by disclosing fee grossness, compatibility_warning semantics, unclassified-pairing behavior, skipped counters, and the meaning of temporal_alignment nulls. No contradiction exists between the description and 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 long but front-loaded with the core purpose and organized into labeled sections (MODES, RESPONSE, SAFETY FIELDS, CODES), which helps an agent parse it. Some repetition, such as restating that both modes use the same matcher, keeps it from being maximally concise, but the verbosity is largely justified by the number of safety caveats.

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, so the description carries the full burden of explaining return values. It covers raw probabilities, top_spreads_pp, compatibility codes, per-entry flags, skipped counters, temporal_alignment semantics, and the fees disclaimer, making the tool usable without additional assumptions. Given the tool's complexity, this is complete.

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 input schema already documents all three parameters with 100% coverage, so the baseline is 3. The description adds meaningful interaction semantics: topic auto-fetches the matching event on each venue, while explicit ticker/slug parameters override the topic-mapped side, and both modes run the same matcher. This exceeds the schema's per-parameter descriptions.

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 clearly identifies the tool as computing the cross-venue spread between Kalshi and Polymarket for the same resolving question, with explicit detail about what the spread means and when it is reliable. It does not explicitly differentiate itself from similarly named siblings like polymarket_arbitrage or polymarket_edges, so it misses the top mark for sibling distinction.

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 explicit invocation guidance: use topic shortcuts for pre-mapped macros or provide kalshi_event_ticker plus polymarket_event_slug for custom pairings, and it warns that pre-mapped topics are not automatically tradeable. It does not explicitly state when to prefer a sibling tool over this one, but the usage context and caveats are clear.

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

A3.5/5.0
Disambiguation2/5

Several tools occupy the same entry-point role (ask_pipeworx, ask_pipeworx_beta, deep_research, discover_tools, suggest_questions), and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The Polymarket family is carefully described but still has heavily overlapping discovery/edge surfaces. Only validate_json_schema is unambiguous, and it has no related siblings to clarify its position.

Naming Consistency2/5

Tool names mix verb-first forms (discover_tools, generate_llms_txt, resolve_entity), noun-first forms (entity_profile, pipeworx_feedback), and bare verbs (remember, recall, forget). Prefix conventions are inconsistent—ask_pipeworx, pipeworx_feedback, polymarket_edges, recent_changes—and almost none of the names reflect the server name Jsonschema.

Tool Count1/5

32 tools is already too many, but for a server named Jsonschema only one tool belongs to that domain; the rest form a broad data-research and prediction-market suite. This is an extreme scoping mismatch: the set is simultaneously oversized and almost entirely off-purpose.

Completeness1/5

For a JSON Schema server, the surface is essentially one operation: validate_json_schema. Common schema lifecycle operations—generation, parsing, conversion, ref resolution, linting—are missing, leaving most JSON Schema tasks impossible. The unrelated data-research tools are internally rich, but they do not complete the apparent domain.