Skip to main content
Glama

Jwt

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

A4.4/5.0
Behavior5/5

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

Annotations already indicate read-only, open-world, idempotent, non-destructive behavior. The description adds significant behavioral details: how data is generated (daily snapshots on cache-miss), what gaps mean, the 60-day TTL bound, and that decay uses daily closes. No contradiction with annotations.

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 long but well-structured: core question, args, response fields, limits. Front-loaded with the main purpose. Each sentence adds value, though some repetition of schema details could be trimmed. Still, it's efficient for its depth.

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 thoroughly covers what the response contains (tracked, expired, snapshot_dates) and their fields. It also explains data gaps and history bounds. For a tool with two parameters and no output schema, this is complete.

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 covers 100% of parameters with descriptions. The description rephrases 'days' as lookback and 'window' as family, but adds minimal new meaning beyond stating defaults and ranges. Given high schema coverage, baseline 3 is appropriate; the description does not extend 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 explicitly states the tool's purpose: tracking edge persistence and decay over time, distinguishing it from raw edge snapshots (polymarket_edges). It uses specific verbs ('track', 'answers') and resource ('edge persistence and decay telemetry'), clearly differentiating from sibling tools like polymarket_arbitrage or polymarket_fill_risk.

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 strong usage context by contrasting a fresh wide edge vs. an old one, implying when to use this tool for deeper analysis. It also explains the trade-off between window families and data availability, though it does not explicitly state when to avoid this tool or use alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Jwt toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation2/5

Multiple tools have unclear boundaries. ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route natural-language queries to the same underlying catalog, with ask_pipeworx_beta currently documented as identical to ask_pipeworx. The Polymarket family (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) also overlaps in purpose, leaving an agent to parse fine-grained differences before selecting.

Naming Consistency3/5

All tool names use snake_case and several share recognizable prefixes (ask_pipeworx_*, polymarket_*), which helps readability. However, the verb/noun order is inconsistent—compare_entities vs entity_profile, bet_research vs deep_research, scan_competitor_ai_presence vs ai_visibility_check—and bare verbs like remember, forget, and subscribe mix with noun-first names.

Tool Count2/5

32 tools is well above the 25-tool threshold for a coherent set. The bloat is worse because the server is named 'Jwt' but only one tool relates to JWT; the rest cover unrelated domains such as data routing, prediction markets, memory, subscriptions, and npm scanning, so the count is neither scoped to the server's name nor internally cohesive.

Completeness2/5

For a server named 'Jwt', the surface is severely incomplete: only decode_jwt is present, with no sign, verify, encode, or refresh tools, so common JWT workflows dead-end. Even when judged as a general Pipeworx data toolkit, the mismatch between the server name and the actual tool surface creates a significant gap for agents expecting JWT functionality.

Resources