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Timeapi Io

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. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "topic": "fed"
+  },
+  {
+    "topic": "btc"
+  }
+]
  2. Added

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already mark the tool as read-only and idempotent, but the description adds substantial behavioral context: it discloses that spreads are gross (not net of Kalshi fees), that compatibility warnings can be non-empty even when matches are returned, that legs with unknown types are never paired, and that temporal alignment null means 'unknown' not 'aligned'. It also explains the meaning of each safety code and flag. No contradictions with annotations exist.

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 well-structured: it opens with the core purpose, then explains modes, response fields, safety codes, and limitations in a logical flow. It front-loads the primary concept and uses clear section divisions (like 'TWO MODES' and 'SAFETY FIELDS'). While verbose, the density of necessary information justifies the length; each sentence carries meaningful content.

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?

There is no output schema, so the description fully compensates by detailing the response structure: leg-by-leg prices, spread arrays, compatibility codes, temporal alignment object, skipped counters, and fee disclaimers. It even explains the meaning of null values and the conditions under which legs are skipped. Given the high complexity of the tool, this description leaves no essential gap for correct invocation and interpretation.

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 description coverage is 100% and the parameter descriptions are clear. The description adds meaning beyond the schema by explaining how the parameters interact (explicit tickers override topic-mapped sides), that both modes run the same matcher, and that the 'topic' parameter has specific pre-mapped values with a warning that they often return warnings. This is valuable context that helps the agent use parameters correctly.

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 states a specific verb ('compute cross-venue spread') and resource ('between Kalshi and Polymarket'), and explicitly differentiates it from sibling tools like polymarket_arbitrage and polymarket_edges by focusing on the same resolving question across two venues. It also clarifies the two modes and the scope of operation, making the tool's identity unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly details TWO MODES—topic shortcuts versus explicit pairings—and instructs when each is appropriate. It further warns that most pre-mapped topics return compatibility warnings, effectively telling the agent when the tool may be unreliable and to expect non-tradeable results. This is clear, actionable usage guidance that goes beyond a simple statement of purpose.

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

B3.2/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all answer natural-language data questions, and ask_pipeworx_beta is explicitly identical to ask_pipeworx right now. The current_time* variants and multiple Polymarket scanners also create real selection ambiguity, though the memory and subscription tools are clearly distinct.

Naming Consistency3/5

Names are mostly lowercase snake_case and readable, but there is no consistent verb_noun pattern: some are imperative (ask_pipeworx, compare_entities, generate_llms_txt) while others are object-first (entity_profile, recent_changes, pipeworx_trending). Subfamilies like current_time* and polymarket_* are internally consistent, but the overall set follows no predictable convention.

Tool Count2/5

Forty tools is far too many for a server named Timeapi Io; only about nine tools actually relate to time zones and current time. The rest form a sprawling Pipeworx research, prediction-market, memory, and subscription platform, making this a mega-bundle rather than a well-scoped toolset.

Completeness3/5

The time-related surface covers current time, zone conversion, zone metadata, and ISO parsing, but lacks common date math or general formatting operations. The Pipeworx side is quite complete for research and fact-checking, but the mixed domain makes coverage uneven and hard to reason about as a single coherent service.

Resources