Skip to main content
Glama

Tweedekamer Nl

by io.github.pipeworx-io
OverviewDiscussionsAdmin

Resolution Audit

resolution_audit
Read-onlyIdempotent

Extract the settlement clause of a single Polymarket (.com or .us) or Kalshi market: who publishes the settling number (source), the clock time + timezone it is taken at (falling back to the market TITLE when the clause itself says "the time range specified in the title" — e.g. Polymarket's 15-minute Chainlink TWAP family), the precision of the computation (e.g. "1-minute candle close" vs "60-second trailing average" vs "twap_60s_lookback"/"twap_30s_lookback" vs "election outcome"), comparison_basis — what is compared to what (end_average_vs_start_average | close_vs_open; orthogonal to precision, since venues can share this comparison shape while computing each endpoint differently), the evidence standard (official_source | consensus_reporting | any_credible_report | unspecified), and void_handling (cancellation/postponement settlement — reused verbatim from bet_research's cancellation_rule detector, not re-derived). Parses Polymarket's description field (fetched via polymarket_market, or via polymarket_us_search for the .us venue) or Kalshi's rules_primary + rules_secondary fields (fetched via kalshi_market) with regex + a small vocabulary — no LLM pass, so an unusual clause reports confidence:"low" rather than a guess. Pass market as a Polymarket slug/URL (either venue) or a Kalshi market ticker (e.g. "KXBTCD-26SEP1317-T66999.99"); a Kalshi EVENT ticker (e.g. "KXBTCD-26SEP1317") also works — it picks one representative market under that event, since the settlement mechanism is normally shared across all strikes/legs in one event. Use this before treating a polymarket_kalshi_spread row as a real arbitrage: two ladders that look alike can settle on different sources (Polymarket.com's Chainlink TWAP stream vs Kalshi/Polymarket US's CF Benchmarks BRTI), at different times, or with a different lookback window — this tool is how you check. Pair with resolution_diff to compare two markets directly. KNOWN GAP: idiosyncratic phrasing that doesn't match the vocabulary returns confidence:"low" and evidence_standard:"unspecified" rather than an LLM-guessed answer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
venueYesWhich venue to fetch the market from. "polymarket" is the offshore venue (polymarket.com); "polymarket-us" is the separate, CFTC-regulated polymarket.us venue, matched by slug via its own search (no exact-slug lookup exists upstream).
marketYesPolymarket (.com or .us) market slug or URL, OR a Kalshi market ticker (preferred) or event ticker (falls back to a representative market under that event).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changed
    • changedInput schema / properties / market / description
      Previous value: -"Polymarket market slug or URL, OR a Kalshi market ticker (preferred) or event ticker (falls back to a representative market under that event)."New value: +"Polymarket (.com or .us) market slug or URL, OR a Kalshi market ticker (preferred) or event ticker (falls back to a representative market under that event)."
    • changedInput schema / properties / venue / description
      Previous value: -"Which venue to fetch the market from."New value: +"Which venue to fetch the market from. \"polymarket\" is the offshore venue (polymarket.com); \"polymarket-us\" is the separate, CFTC-regulated polymarket.us venue, matched by slug via its own search (no exact-slug lookup exists upstream)."
    • changedInput schema / properties / venue / enum
      Previous value: -[
-  "polymarket",
-  "kalshi"
-]New value: +[
+  "polymarket",
+  "kalshi",
+  "polymarket-us"
+]
  2. Added

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already declare a read-only, idempotent, non-destructive operation, but the description adds meaningful behavioral detail: it uses regex plus a small vocabulary rather than an LLM, unusual clauses produce confidence:low instead of guesses, and void_handling is reused from bet_research rather than re-derived. It also discloses a known gap, which is valuable context beyond the structured 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 dense and front-loads the core purpose, with every sentence relevant to a complex domain. It is longer than ideal and relies heavily on parentheticals, but the structure remains readable and information-rich rather than padded.

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 extraction tool with no output schema, the description explains the returned fields, the parsing approach, confidence behavior, and the known limitation. An agent has enough information to call it correctly and interpret its limitations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so both parameters are already documented in the input schema. The description largely repeats the accepted market forms and event-ticker fallback behavior already present in the schema, without adding syntax-level parameter guidance beyond that.

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?

States a specific verb and resource: extract the settlement clause of a single Polymarket or Kalshi market. It then enumerates the exact fields extracted, making it clearly distinct from the sibling resolution_diff tool, which compares two markets.

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?

Gives an explicit when-to-use case: check this before treating a polymarket_kalshi_spread row as real arbitrage. It also names the alternative for direct comparison, resolution_diff, and explains which input forms are accepted.

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

All Tweedekamer Nl toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources