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Glama

Oregon Open Data

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. First observed

TDQS

A4.5/5.0
Behavior5/5

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

The description goes well beyond the readOnly/openWorld/idempotent hints by disclosing the 60-day snapshot TTL, that snapshots are only written on cache-miss (causing data gaps), and that decay is computed from daily closes of edge_pp_net (not intraday). These are important behavioral details not present in annotations, and there is no contradiction.

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 well-structured with clear sections (intro, args, response, limits). Every sentence adds unique information, and the length is justified given the tool's complexity and the absence of an output schema. No fluff or redundancy.

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 takes on the responsibility of explaining return values, and it does so thoroughly: tracked[], expired[], snapshot_dates[], their fields, and data caveats (TTL, gaps, slippage). This is a complete picture for an agent to invoke the tool correctly.

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?

Both parameters are fully described in the input schema (coverage 100%) with defaults and clamping. The description merely repeats these defaults ('default 14, max 30', 'default 1wk') without adding new semantic meaning beyond schema, so the baseline 3 applies.

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?

Description clearly states it provides 'edge persistence and decay telemetry' and answers the question 'how long has this edge existed and is it shrinking?', distinguishing it from sibling polymarket_edges which likely focuses on current edges. The significance is explained with the fresh vs. old wide edge example.

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 clearly frames when to use the tool via the question it answers and the conceptual 'competition clock' median lifespan. It does not explicitly name alternatives or state when not to use it, but the context is clear enough for a user to infer its role relative to siblings like polymarket_edges.

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

B3.4/5.0
Disambiguation1/5

The tool set is a chaotic mix of unrelated domains: Oregon Open Data tools (datasets, metadata, query) are buried among dozens of tools for Pipeworx general query, Polymarket betting, memory management, and AI visibility. Many tools have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim) making it impossible for an agent to distinguish the right tool for a given task without deep inspection.

Naming Consistency1/5

Naming is wildly inconsistent: snake_case (ai_visibility_check, pipeworx_feedback), camelCase (bet_research, datasets, metadata, query), mixed (ask_pipeworx_grounded, polymarket_arbitrage). No consistent verb_noun or pattern exists, and many names are vague (remember, recall, forget) without connection to the server's assumed domain.

Tool Count2/5

33 tools is excessive for a server ostensibly about Oregon Open Data, which only has 3 relevant tools. The remaining 30 are from other services (Pipeworx, Polymarket, etc.) and do not belong, making the count inappropriate for the server's declared purpose.

Completeness2/5

For the Oregon Open Data domain, the surface is bare: only search, metadata, and query. Missing operations like upload, update, or delete datasets. The heavy presence of unrelated tools (betting, memory, AI visibility) does not compensate for the gap in the actual domain coverage.

Resources