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Smithsonian

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

A4.7/5.0
Behavior5/5

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and not destructive. The description adds valuable behavioral details: it reads snapshots with a 60-day TTL, decay numbers come from daily closes not intraday, and gaps in data are explained. No contradictions with 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 somewhat lengthy but well-structured, starting with the main purpose, then detailing parameters and response fields. Every sentence adds value, though it could be slightly more concise without losing clarity.

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?

Given the complexity of the tool (time-series data, multiple output arrays) and no output schema, the description thoroughly explains the return values: tracked[], expired[], snapshot_dates[]. It also covers limitations like TTL and data gaps. The description is self-contained and complete.

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

Parameters5/5

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

Schema description coverage is 100% for both parameters, and the description adds meaningful details beyond the schema: default and max for 'days', default and valid options for 'window'. It also mentions clamping of 'days' to 2-30, which is not in 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 about edge duration and decay, distinguishing it from sibling tools like polymarket_edges (which likely lists current edges) and others focused on different aspects.

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 context on when to use the tool ('a fresh wide edge and a 3-week-old wide edge are different trades') and explains the output structure. However, it does not explicitly compare with alternative tools or state when not to use this tool, so it lacks explicit usage guidelines.

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
Disambiguation3/5

Many tools serve overlapping purposes, such as the three ask_pipeworx variants (stable, beta, grounded) and the six Polymarket-specific tools. While detailed descriptions help distinguish them, an agent could still confuse bet_research with polymarket_edges or the ask_pipeworx versions.

Naming Consistency2/5

Naming is highly inconsistent: verb_noun (ask_pipeworx, compare_entities), noun_noun (bet_research, entity_profile), single verbs (forget, recall, search), and adjective_noun (recent_alerts, deep_research). No clear pattern emerges across the set.

Tool Count3/5

33 tools is on the high side for a server named 'Smithsonian' that actually covers a broad range of data sources (SEC, FDA, Polymarket, npm, etc.). The number feels borderline heavy but is still manageable if the server's true purpose is general research.

Completeness4/5

The tool set covers multiple domains (company financials, drugs, economics, prediction markets, npm, museum data) with reasonable depth. Minor gaps exist, such as lack of PyPI scanning or missing update/delete operations for some memory features, but core workflows are well-supported.

Resources