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Arcgis Lancaster

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).

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and idempotentHint, but the description adds key behavioral context: setting mark_read:true flags events read and affects future calls. It also notes the persistent feed and the raw event payload, providing transparency beyond the structured annotations.

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 sentences, front-loaded with the core purpose, and every sentence earns its place—purpose, payload details, filtering, mark_read behavior, and the alternate endpoint. No fluff or redundancy.

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?

Given 5 parameters and no output schema, the description covers all essentials: purpose, output fields, filtering options, mark_read effect, polling suitability, and an alternative REST access point. The schema fills in parameter specifics, making this a complete and actionable description.

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 detail beyond the schema: a concrete type example ('sec_8k'), clarifies the ISO timestamp format for since, and explains the mark_read side-effect, enhancing parameter understanding.

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,' a specific verb+resource statement that clearly defines the tool's function. It also details the returned payload (source, citation_uri, raw event payload), distinguishing it from sibling tools like list_subscriptions.

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 that this is a read-oriented tool suitable for polling ('Polls work fine') and offers an alternative HTTP endpoint for scripts/dashboards. However, it does not explicitly mention sibling tools or when not to use this tool, missing the full 'when-not/alternatives' guidance.

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

A3.5/5.0
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx and ask_pipeworx_beta are explicitly identical, and ask_pipeworx, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve as query/discovery entry points. Polymarket tools also blur together (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk). While many tools have distinct purposes, these overlapping clusters create real misselection risk.

Naming Consistency2/5

All names are snake_case but the pattern is inconsistent: some are verb_noun (ask_pipeworx, search_datasets, validate_claim), some are noun_verb (query_layer, layer_info is noun_noun), and some are single vague words (forget, recall, remember). No consistent verb_prefix or resource_suffix convention, and the mix of meta-tools vs data tools makes the naming feel arbitrary.

Tool Count2/5

34 tools is far too many for a server ostensibly named 'Arcgis Lancaster' — only 3 tools relate to GIS. The bulk is an unrelated general-purpose data/prediction-market toolkit, making the count excessive for the apparent scope. Even as a broad data toolset, 34 tools is on the heavy side and would benefit from splitting into focused servers.

Completeness2/5

The tool surface is a grab bag with no coherent domain, so completeness is hard to assess and clearly lopsided. GIS functionality has search/query/info but no lifecycle management, while the data side has many query/analysis tools but no create/update/delete operations except for subscriptions and memory. Obvious gaps exist for a 'Lancaster' server (e.g., no layer creation, editing, or spatial analysis tools), and the unrelated tools make the set feel incomplete for any single stated purpose.

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