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Fara

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

A4.1/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds valuable behavioral context: history bounded by 60-day snapshot TTL, data starts from when snapshotting was enabled, decay numbers from daily closes, not intraday. This exceeds annotation coverage without contradicting.

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?

The description is well-structured, starting with a summary, then listing arguments, response structure, and limitations. Every sentence adds value without redundancy. It is front-loaded with the core question, and technical details are organized in separate sections.

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 no output schema, the description comprehensively explains the return structure: tracked[], expired[], snapshot_dates[], with field meanings and edge cases (e.g., median lifespan as competition clock). Limitations are stated. This fully equips the agent to understand and use the tool.

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 description coverage is 100%, so baseline is 3. The description adds minor clarifications: default and max values for 'days', and 'snapshot family' context for 'window'. This slightly enhances understanding but does not significantly exceed 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 clearly states the tool's purpose: providing edge persistence and decay telemetry from daily snapshots. It answers the specific question 'how long has this edge existed and is it shrinking?' with detailed outputs (tracked, expired, snapshot_dates). This distinguishes it from sibling tools like polymarket_edges, which likely focus on current state.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool (to evaluate edge persistence and decay) but does not explicitly state when not to use it or mention alternatives. The example about a fresh wide edge vs. a 3-week-old edge provides context but no direct usage exclusion.

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

A3.8/5.0
Disambiguation2/5

Several tools have heavily overlapping purposes: ask_pipeworx_beta is explicitly identical to ask_pipeworx, and the multiple Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research, polymarket_edge_tracker, polymarket_fill_risk) all surface betting opportunities with only subtle differences. The line between ask_pipeworx, ask_pipeworx_grounded, deep_research, and discover_tools is also fuzzy, making misselection likely.

Naming Consistency3/5

All tool names use snake_case and are readable, but naming conventions are mixed: many follow verb_noun (ask_pipeworx, discover_tools, list_foreign_principals), while others use noun-first or adjective_noun (entity_profile, polymarket_arbitrage, recent_alerts). Several tools share the 'pipeworx_' or 'polymarket_' prefix without that prefix meaning a consistent action type.

Tool Count2/5

34 tools is high for a single MCP server, and the scope spans unrelated domains (general data lookup, prediction-market analytics, FARA registrations, memory, subscriptions, AI-visibility monitoring). This feels like several servers merged rather than one cohesive set; many tools could be split into focused modules without losing functionality.

Completeness4/5

Core workflows are well covered: flexible data querying (ask_pipeworx, grounded, deep_research), entity resolution, FARA search/document retrieval, memory CRUD, subscription lifecycle, and claim validation. There are minor gaps such as no direct tool for single-source parameterized queries (everything routes through the universal router) and no account/profile management, but agents can complete the main advertised tasks.

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