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Wolfram Alpha

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

A4.5/5.0
Behavior5/5

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

The description adds substantial behavioral context beyond the readOnly/idempotent annotations: it explains the 60-day TTL bound, that snapshots are written on cache-miss so gaps mean no scan, and that decay is computed from daily closes rather than intraday. This gives the agent nuanced expectations about data availability and computation.

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?

While the description is long, it is well structured with Args/RESPONSE/LIMITS sections and every sentence conveys essential information. It is front-loaded with the purpose and does not waste words.

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?

Because there is no output schema, the description thoroughly documents the response structure (tracked, expired, snapshot_dates) and their meanings, including edge case behavior. It also explains limitations, making it fully self-contained for invocation.

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% and the description largely repeats the schema (days and window with defaults). No additional semantic meaning is added beyond 'snapshot family' and 'lookback', so the schema already does the heavy lifting.

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 states 'Edge persistence and decay telemetry built from daily polymarket_edges snapshots' and answers a specific question ('how long has this edge existed and is it shrinking?'). This clearly distinguishes it from sibling tools like polymarket_edges by focusing on historical trend analysis 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?

It clearly communicates the intended use case ('how long has this edge existed and is it shrinking?') and contrasts fresh vs old edges to convey when this tracker adds value. However, it does not explicitly name alternative tools or state when not to use it, so it falls short of a 5.

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

A3.7/5.0
Disambiguation2/5

Several tools are near-indistinguishable: ask_pipeworx and ask_pipeworx_beta are currently identical, and ask_pipeworx/ask_pipeworx_grounded/deep_research have overlapping routing behavior. discover_tools vs suggest_questions and ai_visibility_check vs scan_competitor_ai_presence also create boundary ambiguity, making misselection likely for agents.

Naming Consistency2/5

Names mix bare verbs (recall, remember, forget), brand-prefixed nouns (pipeworx_trending, polymarket_edges), and descriptive phrases (generate_llms_txt, scan_competitor_ai_presence). There is no consistent verb_noun or prefix convention, and the server name 'Wolfram Alpha' does not match the dominant pipeworx_/polymarket_ naming.

Tool Count2/5

At 34 tools, the set exceeds the 25+ threshold and feels heavy even for a broad data platform. Several unrelated add-ons (memory trio, ai_visibility, generate_llms_txt, scan_dependency) could live in separate servers, contributing to bloat and diluting the core purpose.

Completeness3/5

The data-research and prediction-market surfaces are fairly thorough (routing, grounded verification, deep research, entity resolution, comparisons, subscriptions), but the Wolfram Alpha core is thin—only short_answer, full_query, and wolfram_compute—with no step-by-step solutions, units catalog, or history. The mismatched server name and unrelated tools indicate an incoherent scope with notable gaps.

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