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Arcgis Dukescounty

by io.github.pipeworx-io

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

A4.7/5.0
Behavior5/5

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

Annotations already mark this as read-only, idempotent, non-destructive, and open-world; the description adds substantial behavioral depth beyond that. It discloses safety fields, compatibility codes, null semantics for temporal_alignment, that unknown legs are never paired, the gross-vs-net fee caveat, and that pairing is keyword-based rather than source-verified. No statement contradicts 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 it is structured into modes, response, safety fields, code explanations, and caveats, with the core purpose front-loaded. Each block adds necessary context, though the opening color about participant pools could be trimmed without losing essential agent guidance.

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?

Given the tool has no output schema, the description does an unusually complete job of explaining what the response contains, how to read safety flags, which comparisons are skipped, when null values appear, and what caveats apply. An agent has enough information to call the tool correctly and interpret results without additional documentation.

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 still adds meaning beyond the schema by explaining the two modes, the exact topic shortcut list, and how kalshi_event_ticker and polymarket_event_slug override the topic-mapped sides. It also clarifies that both modes run the identical token-overlap matcher, which affects how the parameters should be interpreted.

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 first sentence names a specific verb and resource: computing the cross-venue spread between Kalshi and Polymarket for the same resolving question. It clearly differentiates from siblings by emphasizing cross-venue and naming both venues, plus the two modes (topic shortcuts and explicit tickers) make the function 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 concrete guidance on when to use each of the two modes, including the pre-mapped topic list and how explicit tickers override it. It also warns that most pre-mapped topics currently return compatibility_warning and that pre-mapped does not equal tradeable. It does not explicitly name sibling alternatives such as polymarket_arbitrage or polymarket_edges, so an agent must infer when to choose this tool over them.

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

A3.7/5.0
Disambiguation2/5

ask_pipeworx and ask_pipeworx_beta are explicitly identical, and the prediction-market cluster (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) has heavily overlapping purposes that require reading long descriptions to separate. The server name promises ArcGIS Dukes County but most tools are unrelated, making the overall selection space confusing.

Naming Consistency3/5

Most names are snake_case, but conventions vary: some are verb_noun (query_layer, search_datasets, list_subscriptions), some noun-ish (entity_profile, layer_info, pipeworx_trending), some bare verbs (remember, recall, forget, subscribe), and some long compounds (polymarket_edge_tracker, scan_competitor_ai_presence). Readable overall, but no strong consistent pattern.

Tool Count2/5

34 tools is heavy, and only three (search_datasets, layer_info, query_layer) relate to the server's apparent ArcGIS Dukes County purpose. The rest form a sprawling Pipeworx/prediction-market/memory/utility toolkit, creating a severe scope mismatch with the server's name.

Completeness2/5

For the named Dukes County GIS domain, the surface is minimal—dataset discovery, schema, and queries—with no spatial operations or editing, though read-only access may be acceptable. For the broader Pipeworx functionality it is fairly comprehensive, but that is not what the server name advertises, leaving the set incomplete relative to its apparent identity.

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