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Sefaria

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

TDQS

A4.6/5.0
Behavior5/5

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

Annotations indicate read-only, idempotent, non-destructive. The description adds significant behavioral details: explains response structure (tracked, expired, snapshot_dates), limits (60-day TTL, data start), and that decay numbers are based on daily closes. No contradiction 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 comprehensive and well-structured, front-loading the purpose and then explaining the response. While lengthy, every sentence provides valuable information. Minor redundancy in repeating defaults mentioned in schema.

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 edge tracking with time series and no output schema, the description thoroughly explains what the response contains (tracked, expired, snapshot_dates) and limitations (TTL, data start, intraday vs daily). Complete enough for an agent to use correctly.

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

Parameters4/5

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

Schema coverage is 100% with good descriptions for both parameters. The description adds further context (defaults, max for days, clarification of window family) that enhances understanding 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 about edge longevity and distinguishes from sibling tools like polymarket_edges (which likely provides 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 implies usage for historical edge analysis (answering 'how long has this edge existed and is it shrinking?') and mentions the alternative of a fresh edge versus an old one. While not explicit, it provides enough context to understand when to use this tool.

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
Disambiguation2/5

Many tools have overlapping purposes, especially the query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim) and prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges). The Sefaria-specific tools are few, making it hard to distinguish between the core functionality and auxiliary utilities.

Naming Consistency2/5

Tool names follow inconsistent conventions: some use verb_noun (ask_pipeworx, get_text), some noun_verb (ai_visibility_check, bet_research), and some are standalone names (pipeworx_feedback, polymarket_edges). This mix of patterns makes the set appear haphazard.

Tool Count1/5

With 33 tools, the server is heavily overloaded, especially given the name 'Sefaria' which implies a focused set for Jewish text access. The vast majority of tools are unrelated to Sefaria, belonging to the Pipeworx ecosystem, making the count inappropriate for the server's stated purpose.

Completeness1/5

For a server named Sefaria, the tool surface is severely incomplete. Only three tools (get_text, get_commentaries, lookup_ref) directly relate to Jewish texts, missing fundamental operations like search, list books, or manage content. The remaining 30 tools belong to other domains, leaving the core domain under-served.

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