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. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare read-only, idempotent, open-world safety, and the description goes far beyond: it discloses compatibility_warning codes, unverified pairings, skipped_unclassified legs, temporal_alignment null semantics, gross-vs-net fee treatment, and mode-invariant matching behavior. This is a very rich behavioral disclosure with no contradictions.

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 and dense, but front-loaded with purpose and mode selection, followed by structured response, safety, and caveat sections. Some sentences are overloaded with parenthetical enumerations, but nearly every sentence earns its place given the tool's complexity and absence of an output schema.

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 and a complex, nuance-heavy result, the description is exceptionally complete: it documents response fields, compatibility codes, skip logic, per-spread flags, temporal-alignment meaning, fee limitations, and failure modes. An agent can understand both how to call it and how to interpret its output without additional context.

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 substantial meaning: it explains that the topic list auto-fetches matching events on each venue, that explicit ticker/slug parameters enable custom pairings, and that both modes run the identical token-overlap matcher. This goes meaningfully beyond the schema's property 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?

Opens with a specific verb and resource: 'Cross-venue spread between Kalshi and Polymarket for the same resolving question.' It then distinguishes two fully-explained modes (topic vs explicit ticker/slug) and clearly differentiates itself from generic arbitrage tools by emphasizing matched-outcome spread and compatibility screening.

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 practical guidance for when the tool is meaningful ('when the bet shapes are equivalent that delta is a real signal') and warns that 'pre-mapped ≠ tradeable'. It does not explicitly name sibling alternatives or state when to prefer them, but it does provide strong conditions of use and exclusions.

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

A3.7/5.0
Disambiguation2/5

There is significant overlap among many tools: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all serve query-answer purposes with subtle differences, and multiple polymarket tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) cover similar arbitrage/mispricing territory. Additionally, ai_visibility_check and scan_competitor_ai_presence clearly overlap, making it hard to pick the right one.

Naming Consistency2/5

Tool names follow no consistent pattern. Some use a gh_ prefix (gh_get_file, gh_get_repo, etc.), others are descriptive phrases (ask_pipeworx, compare_entities, bet_research), and a few are plain verbs (remember, recall, forget, subscribe, unsubscribe). The mixed conventions and varied verb/noun styles make the set feel disjointed.

Tool Count3/5

At 37 tools, the count is high but still within a usable range. However, the server is named 'Github_private' yet includes only 7 GitHub-specific tools and 30+ unrelated tools (Pipeworx data, Polymarket betting, memory, etc.). This suggests the server aggregates multiple unrelated domains, making the count feel bloated for its apparent GitHub purpose.

Completeness2/5

Given the server name, the GitHub tool surface is severely incomplete: there are no create/update/delete operations for repos, no PR creation or merging, no issue comments, no branches, and no search across repos. The Pipeworx side is fairly comprehensive, but the mismatch between the server name and the actual tool set leaves major gaps for expected GitHub workflows.