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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.

TDQS

A4.7/5.0
Behavior5/5

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

Despite strong annotations (readOnly, openWorld, idempotent, non-destructive), the description adds substantial behavioral detail: the token-overlap matcher is identical in both modes, unclassified legs are never paired, compatibility codes can be non-empty even when matched_pairs>0, the spread is gross and does not model Kalshi fees, and temporal_alignment null means 'could not compute', not 'aligned'. This is far beyond what annotations convey and is exactly the kind of caution an agent needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but exceptionally well structured with labeled sections: modes, response, safety fields, codes, temporal alignment, fees, and skipped-leg counters. The most important usage caveats are front-loaded. Every sentence carries operational meaning; the length is justified by the tool's complexity and 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?

This is a complex read-only tool with no output schema, and the description compensates fully. It explains the response shape (leg-by-leg prices, top_spreads_pp, flags), safety semantics (compatibility_warning, compatibility_codes), edge cases (skipped_unclassified, low_confidence_pairs), fee limitations, and the likelihood that many pre-mapped topics are not currently tradeable. An agent has enough context to invoke it and interpret the result correctly.

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 descriptions already cover all three parameters. The tool description adds meaningful relational semantics: topic maps to a pre-mapped event, while kalshi_event_ticker and polymarket_event_slug override the topic-mapped side. It also lists the exact allowed topic values and gives examples of ticker/slug formats. This is a solid layer of meaning beyond the schema without being redundant.

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 states a specific operation and resource: computing the cross-venue spread between Kalshi and Polymarket for the same resolving question. It goes on to enumerate two clearly distinct modes and lists the 10 pre-mapped topics, so an agent can immediately tell what this tool does and how it is invoked. It is easily distinguishable from sibling tools like polymarket_arbitrage because it names the two specific venues and the delta being measured.

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 mode: use topic shortcuts for pre-mapped events, or explicit ticker/slug pairings for custom events. It also warns that pre-mapped topics are not automatically tradeable and that returned pairs require reading compatibility flags. It does not explicitly name alternative sibling tools or say when NOT to use this tool, so it misses the highest bar, but the guidance is otherwise 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

A3.6/5.0
Disambiguation3/5

Most tools are clearly distinct, but there are several overlapping clusters: ask_pipeworx and ask_pipeworx_beta are near-duplicates today, the Polymarket tools (edges, arbitrage, fill_risk, bet_research) have partially overlapping discovery purposes, and ai_visibility_check vs scan_competitor_ai_presence overlap on single vs multi-entity checks. These overlaps create real misselection risk, though the rest of the set splits cleanly.

Naming Consistency3/5

All names are snake_case and readable, but conventions vary noticeably: verb_noun for actions (check_ip, report_ip, list_subscriptions), domain-prefixed nouns for the Polymarket cluster (polymarket_edges, polymarket_arbitrage), and brand-prefixed meta tools (ask_pipeworx, pipeworx_feedback). Subfamilies are internally consistent, but the overall set mixes patterns.

Tool Count2/5

34 tools is already past the heavy threshold, but the bigger issue is the server name: Abuseipdb should have a handful of IP-abuse tools, yet only 3 of 34 actually relate to AbuseIPDB. The remaining 31 tools form an unrelated Pipeworx/Polymarket/memory suite, making the count wildly inappropriate for the apparent purpose.

Completeness2/5

For the declared AbuseIPDB domain, only check, report, and blacklist are covered; obvious gaps remain like removing/clearing a false report, bulk IP checks, or category metadata. The broader set is a grab bag of unrelated capabilities, so no single domain gets complete lifecycle coverage, and the nominal AbuseIPDB surface is thin and diluted.

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