Skip to main content
Glama

Dexscreener

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

A4.6/5.0
Behavior5/5

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

Annotations already indicate readOnly, idempotent, non-destructive. Description adds significant behavioral details: data source (daily snapshots), decay computation (daily closes, not intraday), TTL limits, and response fields. 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?

Description is detailed but well-organized; every sentence adds value. Slightly long but justified by complex functionality. Front-loaded with purpose and 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?

Despite lacking output schema, description fully compensates with explicit response structure (tracked, expired, snapshot_dates fields and meanings). Parameter descriptions adequate. Annotations cover safety. Limits documented. Complete for this complexity.

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?

Schema covers both parameters with descriptions; description adds constraints (days default 14, clamp 2-30; window default '1wk') and example values. Adds value beyond schema but doesn't fully explain all edge cases.

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 the tool's purpose: tracking edge persistence and decay over time, distinguishing from sibling 'polymarket_edges' which likely provides current edges. Verb 'answers' plus specific question about edge age and shrinkage gives precise purpose.

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 context on when to use (to differentiate fresh vs. old edges) and includes response structure, but lacks explicit when-not-to-use or direct comparison to sibling tools. However, the distinction is implied by naming and description content.

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

All Dexscreener 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

A3.7/5.0
Disambiguation3/5

Several clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve question-answering/research, and the six polymarket_* tools cover closely related prediction-market analysis. Long descriptions help differentiate them, but an agent could easily pick the wrong near-duplicate.

Naming Consistency3/5

All names are snake_case and readable, but there is no consistent verb_noun pattern: some are verbs (search_pairs, validate_claim), some noun phrases (latest_token_profiles, entity_profile), and some prefixes (pipeworx_*, polymarket_*) cover only subsets. The naming is understandable but stylistically mixed.

Tool Count2/5

37 tools is far too many for a server labeled Dexscreener, especially since the majority of tools have nothing to do with DEX data. Even if this is intended as an all-in-one data/research server, the count exceeds what the apparent scope justifies and many tools feel bolted on.

Completeness4/5

For the DEX Screener domain, the core surface is covered: pair lookup, token lookup, search, latest profiles, and boosts. The broader Pipeworx/prediction-market side also has strong coverage with memory, subscriptions, entity resolution, and research tools. Minor gaps exist — some tools feel redundant or exploratory — but there are no critical dead-end workflows.

Resources