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?

The description goes well beyond the readOnlyHint/openWorldHint/idempotentHint annotations: it discloses compatibility warnings, machine-readable codes, unclassified-leg exclusion, temporal alignment semantics, fee treatment, and gross vs net spread. This is exemplary behavioral transparency for a read-only but semantically complex tool.

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 structured with labeled sections (TWO MODES, RESPONSE, SAFETY FIELDS, Codes, fees_note), and the most important purpose statement is front-loaded. Every section conveys non-redundant information required for correct use. It is not concise, but the complexity of the tool justifies the density.

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 the full burden of explaining return values, and it does: leg-by-leg prices, top_spreads_pp, compatibility_codes, skipped_unclassified, low_confidence_pairs, temporal_alignment, and fees_note. For a tool with this many conditional behaviors and edge cases, the description is remarkably 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 schema already documents all three parameters at 100% coverage, so the baseline is 3. The description adds genuine meaning by explaining that explicit parameters override topic-mapped sides, that both modes run the same matcher, and how the topic shortcuts map to events. It could be slightly clearer about whether both explicit parameters must be supplied together, but overall it adds value beyond the schema.

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 clearly states the tool computes a cross-venue spread between Kalshi and Polymarket for the same resolving question, with explicit modes for pre-mapped topics and custom pairings. It distinguishes itself from related tools by naming the venue pair and the matching mechanism. The verb is implied ('returns', 'runs'), but the resource and scope are unambiguous.

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 usage context: use 'topic' for 10 pre-mapped macro shortcuts and explicit ticker/slug for custom pairings. It also warns that pre-mapped topics are not necessarily tradeable. It does not explicitly compare against sibling tools like polymarket_arbitrage, but the mode selection guidance is strong and actionable.

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

A4.3/5.0
Disambiguation5/5

Each tool targets a distinct purpose: data lookup (ask_pipeworx vs deep_research), entity profiles, comparisons, memory, monitoring, and prediction market analysis. Overlaps are minimal and mitigated by explicit usage guidance (e.g., ask_pipeworx vs. ask_pipeworx_grounded vs. deep_research).

Naming Consistency5/5

All tools use descriptive snake_case names following a verb_noun or verb_preposition pattern (e.g., entity_profile, resolve_entity, scan_dependency). The naming is predictable and internally consistent, making it easy for an agent to infer tool purposes.

Tool Count3/5

33 tools is on the high end for typical MCP servers. However, the server covers an exceptionally broad domain (structured data across SEC, FDA, FRED, weather, news, crypto, etc.) and includes meta-tools, monitoring, and memory. The count is justified but may feel excessive for many use cases.

Completeness5/5

The tool surface covers the full lifecycle of data access and analysis: discovery (discover_tools, suggest_questions), retrieval (ask_pipeworx, entity_profile), comparison, claim validation, monitoring, memory, and feedback. There are no obvious gaps for the declared domain, and a feedback tool is provided for missing functionality.