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Climatiq

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

A4.4/5.0
Behavior5/5

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

The description extensively covers behavior beyond annotations: history depth bounded by 60-day TTL, snapshot creation triggers, decay computed from daily closes, and response structure. No contradictions with readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false.

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 a single dense paragraph well-structured with purpose, args, response, and limitations. Every sentence adds value, though slightly verbose.

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?

Without an output schema, the description fully specifies the response format and field meanings. It also covers limitations, parameter defaults, and dependencies. Complete for the tool's complexity.

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 restates defaults and clamps already present in schema. It adds minimal new meaning beyond the schema's parameter descriptions.

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 tracks edge persistence and decay over time, answering specific temporal questions about edges. It distinguishes itself from siblings like polymarket_edges by focusing on historical telemetry rather than current snapshots.

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 for when to use (e.g., to distinguish fresh vs. old edges) but does not explicitly state when not to use or mention alternative tools. The 'Answers...' framing implies usage intent.

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

A4/5.0
Disambiguation3/5

Most tools have distinct jobs and the descriptions are unusually detailed with cross-references, but there is real overlap in the ask_pipeworx family (ask_pipeworx_beta is explicitly identical to ask_pipeworx right now), the Polymarket edge/arbitrage cluster, and some company-research tools. An agent can usually pick correctly, but only after reading long descriptions carefully.

Naming Consistency3/5

All names are snake_case and many are clear verb_noun forms like estimate_emissions or list_subscriptions, but the set also contains descriptive noun phrases (entity_profile, recent_changes, ai_visibility_check), brand-prefixed names (pipeworx_feedback, polymarket_edges), and bare memory verbs (remember, recall, forget). This is a readable but mixed convention rather than one predictable pattern.

Tool Count2/5

34 tools is well above the comfortable ceiling, and the set bundles several unrelated domains: Climatiq emissions, Pipeworx data research, prediction-market analytics, memory, subscriptions, AI visibility, llms.txt generation, and npm dependency checks. It feels heavy and redundant, with ask_pipeworx_beta and the AI-visibility pair as candidates for removal or merging.

Completeness4/5

The core query workflows are well covered: emission factors lead into estimation, general lookups have plain/grounded/deep variants, claim validation and entity profiles exist, and subscriptions/memory have full lifecycles. The main gaps are minor—no batch emissions endpoint or direct order execution—so agents can work around them.

Resources