Skip to main content
Glama

Maryland 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

A4.2/5.0
Behavior4/5

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

Annotations already indicate the tool is read-only and idempotent. The description adds valuable behavioral details: it reads from daily snapshots, uses only the latest snapshot for tracked data, and includes limitations like 60-day TTL and decay from daily closes.

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 front-loaded with the key question and well-structured with sections for RESPONSE and LIMITS. It is informative but could be slightly more concise.

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?

Without an output schema, the description comprehensively explains the response structure (tracked[], expired[], snapshot_dates[]) and includes limitations. For a tool with two optional parameters, this is thorough and covers all necessary context.

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 parameters are fully documented in the schema. The description adds usage context (defaults, clamps, snapshot family) but does not add significant meaning 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 provides edge persistence and decay telemetry from daily snapshots, answering the specific question of how long an edge has existed. It distinguishes from sibling tools like polymarket_edges by focusing on historical trends rather than current edges.

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 this tool: to differentiate between fresh wide edges and old wide edges. It implicitly contrasts with `polymarket_edges`, but does not explicitly state when not to use it or list alternatives.

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

All Maryland Open Data 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

A3.7/5.0
Disambiguation3/5

Tools are generally distinct in purpose, but the server name 'Maryland Open Data' conflicts with the inclusion of many unrelated Pipeworx tools (e.g., prediction market tools). This creates ambiguity about the server's actual domain, making it hard for agents to know what to expect.

Naming Consistency2/5

Naming conventions are mixed: some tools use snake_case (ai_visibility_check), others are plain (datasets, query), and some are descriptive phrases (ask_pipeworx_grounded). No consistent verb_noun pattern emerges, leading to a chaotic feel.

Tool Count2/5

33 tools is high, and the majority are unrelated to Maryland Open Data, suggesting scope creep. The server tries to be a general-purpose data platform but is named after a specific dataset, making the count feel excessive and unfocused.

Completeness3/5

The Maryland Open Data subset is minimal (3 tools: datasets, metadata, query), lacking update/delete/CRUD operations. The broader set includes many query and analysis tools, but the server's stated purpose is not fully covered.

Resources