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Abn Lookup

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

A4.5/5.0
Behavior5/5

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

The description goes far beyond the readOnly/idempotent annotations by disclosing fee modeling limitations, compatibility warnings, unclassified legs never being paired, temporal-alignment semantics, and the fact that all returned spreads are gross rather than net tradeable edge. It also explains that compatibility fields must be read even when matched_pairs > 0.

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 highly structured with labeled sections and front-loaded purpose. It contains redundant emphasis around safety fields and warnings, and the extended code list adds bulk, but almost every sentence carries needed operational detail, especially given the 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?

For a tool with no output schema, the description is unusually complete: it covers the response shape, per-leg pricing, spread direction, compatibility codes, skipped categories, temporal alignment meaning, fee caveats, and statistical rarity of real spreads. An agent has enough context to invoke the tool and interpret results safely.

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 substantial meaning beyond the schema: it explains that topic auto-fetches the matching event, that kalshi_event_ticker and polymarket_event_slug override the topic-mapped side, and that both modes run the identical token-overlap matcher. This materially improves an agent's ability to choose and combine parameters correctly.

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 uses a specific verb and resource: "Cross-venue spread between Kalshi and Polymarket for the same resolving question." It clearly explains what the tool computes and gives two concrete operating modes. It does not explicitly name and contrast sibling tools like polymarket_arbitrage, so it stops short of full sibling differentiation.

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: either use a pre-mapped topic shortcut or supply explicit event identifiers, and it warns that pre-mapped does not imply tradeable. It does not explicitly state when to prefer this tool over alternatives or when not to use it, but the mode descriptions make the intended invocation path 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

A3.8/5.0
Disambiguation2/5

Several tools have unclear or overlapping boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, while ask_pipeworx, deep_research, and validate_claim all handle factual lookup/research tasks. The five polymarket_* tools plus bet_research also overlap enough that an agent could easily pick the wrong entry point despite verbose descriptions.

Naming Consistency3/5

All names are snake_case and generally descriptive, but conventions are mixed: some are verb-first (ask_pipeworx, resolve_entity, validate_claim), some are noun-first (abn_lookup, entity_profile, polymarket_edges), and prefixes like pipeworx_ and polymarket_ are used inconsistently. It is readable but not a clean, predictable pattern.

Tool Count2/5

34 tools is far too many for a server named 'Abn Lookup' — most of the surface is a broad data-research platform with prediction-market analysis, memory, subscriptions, feedback, and web utilities. The count could fit a large platform, but under this server name and with several near-duplicate entry points, it feels bloated.

Completeness4/5

For a read-only lookup/research server, coverage is strong: ABR lookups, entity resolution, single-query research, grounded verification, deep research, company profiles, comparisons, change feeds, prediction-market analysis, memory, and subscriptions are all represented. Minor gaps exist (e.g., no ACN search-by-name, no order execution), but no core workflow dead-ends.