Skip to main content
Glama

Devdocs 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. Added

TDQS

A4.6/5.0
Behavior5/5

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

This is exactly the kind of behavioral context that annotations alone cannot convey, and it directly helps the agent interpret results correctly.

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 deliberately dense, and each paragraph earns its place: purpose/modes first, then response shape, then safety codes, then caveats and fees. It is front-loaded with the core purpose before diving into detail. It could be pared slightly, but the complexity of the tool justifies much of the length.

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 complex comparison tool with no output schema, this description is unusually complete: it names the response fields (leg-by-leg prices, top_spreads_pp, compatibility_warning, compatibility_codes[], skipped_unclassified, low_confidence_pairs[], temporal_alignment, fees_note), defines the meaning of null values, and warns about skipped cross-type/cross-subtype comparisons. An agent has enough context to call the tool and interpret its output 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 description coverage is 100%, so the baseline is 3. The description adds meaningful semantics beyond the schema: it explains that BOTH modes run the identical token-overlap matcher, that explicit parameters override the topic-mapped side, and that explicit mode requires custom pairings. It also clarifies how 'topic' values map to auto-fetched events. This is more than the schema alone provides.

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?

It names the specific resource (spread between two named venues), explains the delta's meaning, and distinguishes the two operational modes (topic shortcuts vs explicit ticker/slug pairings). It is clearly distinct from siblings like polymarket_arbitrage by focusing on matched-outcome spread with compatibility disclosures.

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?

However, it does not explicitly contrast with alternative tools (e.g., polymarket_arbitrage or polymarket_edges) or state when not to use this tool in favor of a sibling, so it falls short of a full when/when-not guide.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Devdocs Io toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3.1/5.0
Disambiguation3/5

There are many tools with overlapping purposes (e.g., multiple ways to ask questions, multiple Polymarket analysis tools, multiple company lookup tools). The detailed descriptions help distinguish them, but an agent could still easily select the wrong one.

Naming Consistency2/5

Tool names are inconsistent, mixing verb_noun patterns (ask_pipeworx, search_docs) with single words (db, docs) and compound names (polymarket_arbitrage, ai_visibility_check). No clear convention across the set.

Tool Count2/5

37 tools is excessive for a DevDocs documentation server; most tools are unrelated to documentation (Pipeworx data, memory, subscriptions). The core documentation functionality only requires about 7-8 tools, making the rest feel extraneous.

Completeness3/5

For the core DevDocs functionality, the tools cover listing, searching, and fetching documentation. However, the server includes many unrelated tools that are incomplete on their own (e.g., only some data lookups, no CRUD for prediction markets). Thus overall completeness is mediocre.

Resources