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Arcgis Dukescounty

by io.github.pipeworx-io

Recent Alerts

recent_alerts
Read-onlyIdempotent

Pull fired events from your subscription feed. Returns the most recent alerts the evaluator has written to your persisted feed — each carries source, citation_uri (pipeworx:// when available), and the raw event payload. Filter by type (e.g. "sec_8k") and/or since (ISO timestamp). Set mark_read:true to flag returned events read so the next call only shows newer ones. Polls work fine; the same feed is also at GET registry.pipeworx.io/alerts.json for scripts and dashboards.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoOptional — filter to one subscription type.
limitNoMax events to return (1-200, default 50).
sinceNoOptional ISO timestamp — return events fired_at >= this time.
mark_readNoFlag the returned events read in the same call (default false).
unread_onlyNoReturn only events where read_at is null (default false).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, lowering the bar. The description adds valuable behavioral context by explaining that mark_read:true flags events as read and affects subsequent calls, plus noting the feed is persisted and includes citation_uri. This goes beyond what annotations state.

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 three tight sentences: states the core action, lists key return fields and filters, then gives a poll-ability note and an alternative access point. Every sentence earns its place, with no filler or repetition of schema details.

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 appropriately describes return payload components. It covers the main parameters, mark_read side effects, and even provides an external endpoint for automation. Given the moderate complexity (5 optional params, persisted feed), this is fully complete for an agent to select and invoke the tool.

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 coverage is 100%, so the baseline is 3, but the description adds meaningful elaboration: it gives a concrete example type ("sec_8k"), explains mark_read's side effect, and clarifies the since parameter as an ISO timestamp. This exceeds the schema descriptions without becoming redundant.

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 pulls fired events from the subscription feed, with a specific verb ('Pull') and resource ('fired events from your subscription feed'). It also describes the return payload (source, citation_uri, raw event payload), distinguishing it from siblings like list_subscriptions which manage subscriptions themselves.

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 gives clear context: it is for reading alerts, supports polling, and mentions an alternative HTTP endpoint for scripts/dashboards. However, it does not explicitly contrast with sibling tools or state when not to use it, so it falls short of a 5.

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

All Arcgis Dukescounty toolsInspect
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TDQS

A3.7/5.0
Disambiguation2/5

ask_pipeworx and ask_pipeworx_beta are explicitly identical, and the prediction-market cluster (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker, polymarket_kalshi_spread) has heavily overlapping purposes that require reading long descriptions to separate. The server name promises ArcGIS Dukes County but most tools are unrelated, making the overall selection space confusing.

Naming Consistency3/5

Most names are snake_case, but conventions vary: some are verb_noun (query_layer, search_datasets, list_subscriptions), some noun-ish (entity_profile, layer_info, pipeworx_trending), some bare verbs (remember, recall, forget, subscribe), and some long compounds (polymarket_edge_tracker, scan_competitor_ai_presence). Readable overall, but no strong consistent pattern.

Tool Count2/5

34 tools is heavy, and only three (search_datasets, layer_info, query_layer) relate to the server's apparent ArcGIS Dukes County purpose. The rest form a sprawling Pipeworx/prediction-market/memory/utility toolkit, creating a severe scope mismatch with the server's name.

Completeness2/5

For the named Dukes County GIS domain, the surface is minimal—dataset discovery, schema, and queries—with no spatial operations or editing, though read-only access may be acceptable. For the broader Pipeworx functionality it is fairly comprehensive, but that is not what the server name advertises, leaving the set incomplete relative to its apparent identity.

Resources