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Arcgis Charlotte

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

A4.6/5.0
Behavior5/5

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

Despite annotations already covering safety, the description adds significant behavioral detail: alerts carry citation_uri and raw payload, mark_read mutates read-state so next call returns newer events, polling is safe, and the same feed is available via HTTP. This goes well beyond the annotations and is transparent about side effects.

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?

Five concise sentences, front-loaded with the purpose. Every sentence delivers distinct value (return type, fields, filtering, mark_read, polling/HTTP alternative) without redundancy. Highly efficient for the information it conveys.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers what the tool returns (source, citation_uri, raw payload), filtering, mark_read behavior, and even an external fallback. With no output schema, it adequately describes return items. Slight gap: it doesn't explain how unread_only interacts with mark_read, but the schema covers that parameter.

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 baseline is 3. The description enriches meaning by exemplifying type (e.g. 'sec_8k'), clarifying the since parameter as ISO timestamp, and explaining the consequence of mark_read (next call shows only newer events). It does not cover limit or unread_only, but those are well-defined in 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 opens with 'Pull fired events from your subscription feed,' which clearly states the tool's action and resource. It distinguishes this tool from siblings like list_subscriptions by focusing on fired alerts, not subscriptions themselves, and mentions specific payload fields.

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?

It provides concrete usage context: filtering by type/since, using mark_read for polling, and explicitly notes an alternative HTTP endpoint for scripts/dashboards. However, it does not contrast with sibling tools like recent_changes, but the polling guidance and endpoint alternative are sufficient.

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 are easy to confuse: ask_pipeworx_beta is currently identical to ask_pipeworx, deep_research overlaps heavily with ask_pipeworx/ask_pipeworx_grounded, and the six polymarket_* tools have closely related purposes. ai_visibility_check and scan_competitor_ai_presence also overlap, making selection error-prone.

Naming Consistency4/5

Almost all tools use lowercase snake_case and mostly follow a verb_noun pattern (query_layer, resolve_entity, scan_dependency, validate_claim). A few noun-style names like entity_profile, layer_info, and pipeworx_trending deviate slightly, but the overall convention is predictable.

Tool Count2/5

34 tools is well past the heavy threshold, and the server is named 'Arcgis Charlotte' while only three tools actually relate to ArcGIS. The rest form a sprawling Pipeworx research, prediction-market, subscription, and memory toolkit, which creates a severe scope mismatch.

Completeness2/5

For the Pipeworx data side the surface is quite thorough, but for the declared ArcGIS Charlotte purpose it is thin: search_datasets, layer_info, and query_layer provide read-only access with no update/delete, analysis, or dataset management. The tool set therefore has a significant gap relative to its stated domain.

Resources