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Arcgis Puyallup

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

A3.7/5.0
Behavior1/5

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

The description discloses a behavioral trait that contradicts the annotations. The annotation readOnlyHint is true, but the description explicitly describes a state-changing action: 'Set mark_read:true to flag returned events read so the next call only shows newer ones.' This is a mutation, directly contradicting the read-only hint. No credit can be given for transparency when the description itself undermines the annotation.

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 long, front-loaded with the core action, and every sentence adds useful detail. There is no redundancy or filler. It efficiently covers the return payload, filtering, mark_read behavior, and even notes an alternative access method.

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

Completeness3/5

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

The description covers many important aspects: what is returned, how to filter, mark_read semantics, and polling friendliness. However, it does not explicitly address the unread_only parameter or clarify the feed's retention/scope, and it contradicts the readOnlyHint annotation, creating overall incompleteness. The contradiction means the description cannot be fully trusted as a complete behavioral guide.

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?

The schema already provides 100% coverage with descriptions for all parameters, so the baseline is 3. The description adds value by explaining the effect of mark_read ('so the next call only shows newer ones') and giving an example for type ("sec_8k"). This goes beyond the schema's simple flag description, 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 clearly states the tool's purpose: 'Pull fired events from your subscription feed.' It specifies the resource (subscription feed), the action (pull), and the nature of the data (most recent alerts). It also distinguishes itself from siblings like list_subscriptions by focusing on the fired events feed.

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 provides clear usage context: it can be polled, filtered by type and since, and offers mark_read for read-tracking. It also mentions an alternative HTTP endpoint for scripts/dashboards. However, it does not explicitly contrast with sibling tools like recent_changes or list_subscriptions, which would make the guidance more complete.

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

A3.6/5.0
Disambiguation2/5

Several tool clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve research questions through the same router, and the five polymarket_* tools plus bet_research form a dense prediction-market cluster. Even with detailed descriptions, an agent choosing between these near-synonyms would frequently need extra reasoning or make the wrong pick.

Naming Consistency2/5

Names mix product-prefixed verbs (ask_pipeworx, polymarket_arbitrage), generic verbs (remember, forget, recall, subscribe), and noun phrases (entity_profile, layer_info, recent_alerts). There is no consistent verb_noun or prefix convention across the set, making the tool surface feel patchwork rather than systematically named.

Tool Count2/5

With 34 tools, the count is already on the heavy side, but it is especially mismatched with the server name 'Arcgis Puyallup': only search_datasets, query_layer, and layer_info actually belong to that GIS domain. The rest are a broad Pipeworx research and prediction-market platform, so the set feels bloated and off-scope for the apparent purpose.

Completeness3/5

The ArcGIS read-only surface is minimally reasonable: search, schema inspection, and querying cover basic open-data consumption. The Pipeworx side is quite rich, with memory, subscriptions, lookups, grounded verification, and discovery, but the named GIS domain is thinly served and lacks obvious capabilities like listing all datasets or browsing layers without a keyword. Overall, coverage is uneven and hard to evaluate cleanly because the server mixes two unrelated purposes.

Resources