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Data Centrevaldeloire

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

A4.6/5.0
Behavior5/5

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

Adds significant depth beyond annotations: explains snapshot mechanism, meaning of trend/decay values, distinction between tracked and expired opportunities, and limitations (60-day TTL). No contradiction with annotations (readOnlyHint, etc.).

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?

Well-structured: front-loaded summary, then detailed response fields. Every sentence adds value. Could be slightly more concise with bullet points for response format, but no wasted text.

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?

Thoroughly explains response fields (tracked, expired, snapshot_dates) and limitations. No output schema exists, so description compensates fully. Complete for agent to understand tool behavior and results.

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?

Both parameters (days, window) are fully described in schema (100% coverage). Description adds extra context: defaults, clamps, and meaning of window in response. Adds value but schema already covers basics.

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, answering 'how long has this edge existed and is it shrinking?'. Distinguishes from siblings like polymarket_edges by focusing on historical time-series 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?

Provides strong context on when to use: to differentiate fresh vs aged edges. Implicitly suggests not to use for current edge data, but does not explicitly name alternatives. Could be improved by stating 'for current edges, use polymarket_edges'.

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
Disambiguation2/5

Several tools have overlapping boundaries, especially ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim, all of which route through the same underlying source catalog. The beta variant is explicitly identical to the stable router right now, which forces agents to pick between tools that currently do the same thing. Many of the polymarket tools are also close enough that an agent must read long descriptions carefully to avoid mis-selection.

Naming Consistency3/5

The set is readable and mostly snake_case, but the naming conventions are mixed: some tools use verb_noun (search_datasets, compare_entities, validate_claim), some are noun-like (entity_profile, dataset_info, bet_research), and some use product prefixes (pipeworx_trending, polymarket_edges). There is no single consistent pattern, though related clusters are internally recognizable.

Tool Count2/5

Thirty-four tools is above the heavy threshold, and the apparent server purpose from the name is a regional open-data portal, which only needs the three dataset tools. The remaining tools are a sprawling Pipeworx research assistant covering prediction markets, npm dependencies, AI visibility, memory, subscriptions, and feedback, making the set feel overstuffed and poorly scoped for the stated server.

Completeness4/5

Within the broad data/research scope the coverage is quite deep: discovery, querying, grounded verification, entity profiling, comparisons, monitoring, subscriptions, memory, and prediction-market analysis all have dedicated tools. The Centre-Val de Loire core is adequately covered by search_datasets, dataset_info, and query, though a raw download or full-catalog listing endpoint is missing. The real weakness is not missing lifecycle steps but unclear boundaries between overlapping meta-tools.

Resources