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Norges Bank

by io.github.pipeworx-io

Polymarket Edge Tracker

polymarket_edge_tracker
Read-onlyIdempotent

Edge persistence and decay telemetry built from daily polymarket_edges snapshots. Answers "how long has this edge existed and is it shrinking?" — a fresh wide edge and a 3-week-old wide edge are different trades (the latter is wide for a reason nobody is willing to take). Args: days (lookback, default 14, max 30), window (snapshot family, default "1wk"). RESPONSE: tracked[] = every opportunity in the LATEST snapshot with its full edge_pp_net time-series across prior snapshots, first_seen, trend (new | widening | stable | decaying) and decay_pp_per_day (both computed on |edge_pp_net| — the value itself is signed by trade direction, negative = SELL YES); expired[] = opportunities that appeared in earlier snapshots but are GONE from the latest (closed, resolved, or arbed away) with their lifespan_days — the median lifespan is your competition clock; snapshot_dates[] = which days actually have data (snapshots are written when polymarket_edges runs on a cache-miss, so gaps mean nobody scanned that day). LIMITS: history depth is bounded by the 60-day snapshot TTL and starts from when snapshotting was enabled; decay numbers come from daily closes of edge_pp_net (net of default slippage), not intraday.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoLookback in days (default 14, clamp 2-30).
windowNoWhich polymarket_edges window family to read snapshots for: 24hr | 1wk | 1mo (default 1wk).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added
  2. Removed
  3. Added

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the annotations (readOnly, idempotent, openWorld), the description discloses extensive behavioral details: how snapshots are created (cache-miss), 60-day TTL limitation, the meaning of date gaps, computation on daily closes vs intraday, decay on |edge_pp_net|, negative sign indicating SELL YES, and the fate of expired opportunities. This is far beyond basic safety hints.

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 with clear sections (purpose, Args, RESPONSE, LIMITS) and front-loaded intent. The length is justified because there is no output schema, so the detailed response breakdown is necessary. Minor redundancy exists in the Args section which repeats schema info.

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?

Since there is no output schema, the description fully compensates by detailing the response structure (tracked[] with trend and decay fields, expired[] with lifespan_days, snapshot_dates[]). It also covers data source, TTL limits, and calculation specifics, making the tool's behavior fully predictable.

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?

The input schema already provides full descriptions for both parameters (days: lookback, default 14, clamp 2-30; window: 24hr | 1wk | 1mo, default 1wk). The description's 'Args' section merely restates this information without adding deeper semantics or usage nuances, so it adds no value beyond the schema.

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 clearly states the tool's purpose: 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots.' It answers a specific question ('how long has this edge existed and is it shrinking?') and differentiates from sibling tools like polymarket_edges by focusing on time-series persistence rather than current edge values.

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 provides clear context for when to use the tool: 'a fresh wide edge and a 3-week-old wide edge are different trades (the latter is wide for a reason nobody is willing to take).' This implies using the tool to evaluate edge age and decay before trading. It does not explicitly name alternative tools or state 'when not to use', but the context is sufficient.

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

A3.9/5.0
Disambiguation4/5

Most tools have distinct purposes (e.g., entity_profile vs compare_entities), but there is some overlap between ask_pipeworx, ask_pipeworx_grounded, and deep_research, as well as among polymarket tools, which could cause misselection.

Naming Consistency2/5

Tool names are inconsistent, mixing verb_noun (list_flows, get_series) with descriptive names (ask_pipeworx, bet_research) and varying conventions (snake_case vs no underscores).

Tool Count3/5

33 tools is on the high side but manageable for a broad platform; however, the server name 'Norges Bank' suggests a narrower scope, making the count feel bloated.

Completeness2/5

For a server named 'Norges Bank', many tools are irrelevant (polymarket, pipeworx meta-tools, memory, etc.), leaving significant gaps in core Norwegian banking data coverage.

Resources