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Sgd

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).

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already indicate read-only and idempotent behavior. The description adds rich detail: the structure of tracked[], expired[], snapshot_dates[], and limitations like snapshot TTL and decay from daily closes. No contradictions.

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 well-structured with a clear intro, response sections, and limitations. It is somewhat long but every sentence contributes value. Front-loaded with the key question.

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 (time-series, decay, expiration) and no output schema, the description thoroughly explains the response fields, including edge cases like gaps and median lifespan. It 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% with descriptions for both parameters. The description adds default values and clamp range (already in schema), but does not significantly enhance parameter 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 starts with 'Edge persistence and decay telemetry' and explicitly states the tool answers 'how long has this edge existed and is it shrinking?'. It clearly distinguishes itself from sibling tools like polymarket_edges by focusing on time-series analysis.

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 when to use it (to assess edge freshness), but does not explicitly state when not to use it or offer alternatives. It contrasts fresh vs aging edges, providing good context.

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

A3.6/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta (explicitly identical today), ask_pipeworx_grounded, deep_research, and validate_claim all route factual questions through similar pipelines. The polymarket_* cluster also blurs together, with arbitrage, edges, fill_risk, kalshi_spread, and bet_research all analyzing prediction-market mispricings from different angles.

Naming Consistency3/5

All names use consistent snake_case, but the verb/noun pattern is mixed: some are verb-first (compare_entities, generate_llms_txt, validate_claim), others noun-first (polymarket_edges, entity_profile, ai_visibility_check), and some are bare product names (ask_pipeworx, pipeworx_trending). Readable overall, but no single predictable convention.

Tool Count2/5

34 tools is far too many for a coherent server, especially since the server is named 'Sgd' but only 3 tools relate to yeast genetics. The remaining 31 tools span data lookup, prediction markets, memory, subscriptions, npm scanning, and llms.txt generation—an unfocused grab bag that should be split into multiple servers.

Completeness2/5

As an SGD yeast-genome server, the surface is thin: search, get_gene, and get_gene_go cover basic lookup but miss sequences, interactions, strains, homologs, and other standard SGD data. As a general data utility, the collection is broad but incoherent, with several one-off tools (generate_llms_txt, scan_dependency) that have no connection to the rest.

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