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NYC 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).

TDQS

A4.5/5.0
Behavior5/5

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

Despite annotations already indicating readOnly, idempotent, and non-destructive, the description adds critical behavioral details: history depth bounded by 60-day TTL, decay computed from daily closes not intraday, snapshot gaps meaning no scan that day, and how expired opportunities are tracked. This goes well beyond what annotations provide.

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?

Description is long but every sentence adds value. It is well-structured with sections for RESPONSE and LIMITS, front-loads the core purpose, and avoids redundancy. No wasted 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?

With no output schema, the description fully explains the response structure (tracked[], expired[], snapshot_dates[]) and the meaning of each field (e.g., trend values, decay_pp_per_day, lifespan_days). This provides complete context for an agent to understand what the tool returns.

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 both parameters described. The description reiterates defaults and bounds (days: default 14, clamp 2-30; window: default '1wk') but does not add significant new semantics beyond the schema. Baseline 3 is appropriate.

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?

Description clearly states it provides 'edge persistence and decay telemetry' from daily snapshots, answering the specific question about how long an edge has existed and whether it's shrinking. This distinguishes it from sibling tool polymarket_edges which likely just provides current snapshot data.

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 by contrasting fresh wide edges vs. 3-week-old wide edges. It implies that this tool should be used when historical edge persistence matters but does not explicitly list alternatives or exclusion criteria.

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 have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical routers, and the five polymarket_* tools all perform prediction-market analysis. Even with detailed descriptions, an agent could easily select the wrong one, especially since the server name 'NYC Open Data' does not hint at this focus.

Naming Consistency4/5

Most tools follow a consistent lowercase snake_case verb_noun pattern (e.g., list_subscriptions, generate_llms_txt, resolve_entity) and families share prefixes like polymarket_ and pipeworx_. Minor deviations exist with one-word names like datasets, metadata, and forget, but overall the naming is readable and predictable.

Tool Count2/5

34 tools is far too many for a server labeled 'NYC Open Data,' where only 3 tools (datasets, metadata, query) actually serve that purpose. The rest form a general-purpose data platform, but even then the count is high and includes many redundant meta-tools and overlapping Polymarket utilities, making the set feel bloated.

Completeness3/5

For the NYC Open Data domain, the three dedicated tools cover search, metadata, and query—adequate core functionality but missing export or dataset management capabilities. For the broader Pipeworx platform, coverage is strong (routing, grounded answers, deep research, entity profiles, subscriptions, memory), but the severe mismatch between the server name and actual scope leaves a major completeness gap.

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