Skip to main content
Glama

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

A4.6/5.0
Behavior5/5

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

Even with readOnlyHint/openWorldHint/idempotentHint covering safety, the description adds substantial operational behavior beyond annotations. It explains matcher behavior, compatibility codes, null semantics for temporal_alignment, gross-vs-net fee treatment, and skipped leg counters. It also flags that compatibility fields can be non-empty even when matched_pairs>0, preventing a dangerous misinterpretation.

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 organized with labeled sections (modes, response, safety fields, codes, fees) and front-loads the core purpose and the two modes. It could be tightened slightly because null semantics and compatibility codes are described twice, but no sentence is pure filler.

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 carries full responsibility for return semantics, and it delivers: leg-by-leg prices, top_spreads_pp, compatibility_warning/codes, per-spread flags, temporal_alignment, fees_note, and skipped counters are all explained. It also covers the main edge case an agent would encounter—pre-mapped topics are often not currently tradeable.

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, but the description adds value by explaining that explicit tickers override the topic-mapped side and that both modes run the identical token-overlap matcher. This clarifies interactions between the otherwise independently documented parameters.

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 by defining the tool as a 'Cross-venue spread between Kalshi and Polymarket for the same resolving question,' which names the exact resource and scope. It then enumerates two concrete modes and the response shape, making it easy to distinguish from Polymarket-only sibling tools.

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 clear context for both modes: use topic shortcuts for pre-mapped macros and explicit Kalshi/Polymarket identifiers for custom pairings. It also warns that 'pre-mapped ≠ tradeable' and that most topics return compatibility warnings. However, it never explicitly names alternative sibling tools or states when to use them instead.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

Many tools overlap in purpose: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all provide data retrieval with subtle differences. The Polymarket suite (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) and AI visibility tools (ai_visibility_check, scan_competitor_ai_presence) also have fuzzy boundaries. While some tools are clearly distinct, the overall set has significant ambiguity that could lead to misselection.

Naming Consistency2/5

Tool names mix conventions: some are verb_noun (search_networks, compare_entities, remember, forget), others are noun_compound (entity_profile, polymarket_edges, ask_pipeworx), and a few are verb-only (recall, forget). The pattern is inconsistent, with no clear naming strategy across the tool surface.

Tool Count2/5

With 34 tools, the server is heavily overloaded. While it serves as a general data platform, many tools are meta-level (discover_tools, suggest_questions) or peripheral (subscriptions, memory). The count feels excessive for the core purpose, and many tools could be consolidated or removed.

Completeness1/5

Despite the server name 'Peeringdb', only three tools (search_networks, search_facilities, search_exchanges) directly serve that domain. The vast majority of tools cover unrelated areas (Pipeworx data, Polymarket betting, AI visibility, memory, subscriptions). For the declared purpose of PeeringDB, the surface is severely incomplete—missing common operations like retrieving network details, viewing IX members, or managing peering policies.