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Glama

Maryland Open Data

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

A4.1/5.0
Behavior2/5

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

Annotations declare readOnlyHint: true, but the description states that setting mark_read:true will 'flag returned events read,' which is a mutation. This is a direct contradiction. Additionally, the description does not disclose other behavioral traits like rate limits or authentication needs, but the annotations already cover idempotency and non-destructiveness.

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 four sentences long, front-loaded with the core purpose, and each sentence adds value (filtering, mark_read, polling, alternative access). No fluff; every sentence earns its place.

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 no output schema, the description explains the return structure (source, citation_uri, payload). It covers all parameters, provides usage examples, and mentions alternative access. For a tool with 5 optional parameters and no required fields, this is complete.

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?

With 100% schema description coverage, baseline is 3. The description adds meaningful context: explains type with example ('sec_8k'), specifies ISO timestamp format for since, clarifies mark_read and unread_only semantics. It slightly improves understanding beyond 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 purpose: 'Pull fired events from your subscription feed. Returns the most recent alerts...' It uses specific verbs and resource names, and the scope is well-defined. It distinguishes itself from sibling tools like list_subscriptions or subscribe by focusing on retrieving alert events.

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 guidance on filtering by type and timestamp, using mark_read for state management, and notes that polling is fine. It also mentions an alternative access method (direct URL). However, it does not explicitly state when to avoid using this tool or compare with siblings for similar functionalities.

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

All Maryland Open Data toolsInspect
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TDQS

A3.7/5.0
Disambiguation3/5

Tools are generally distinct in purpose, but the server name 'Maryland Open Data' conflicts with the inclusion of many unrelated Pipeworx tools (e.g., prediction market tools). This creates ambiguity about the server's actual domain, making it hard for agents to know what to expect.

Naming Consistency2/5

Naming conventions are mixed: some tools use snake_case (ai_visibility_check), others are plain (datasets, query), and some are descriptive phrases (ask_pipeworx_grounded). No consistent verb_noun pattern emerges, leading to a chaotic feel.

Tool Count2/5

33 tools is high, and the majority are unrelated to Maryland Open Data, suggesting scope creep. The server tries to be a general-purpose data platform but is named after a specific dataset, making the count feel excessive and unfocused.

Completeness3/5

The Maryland Open Data subset is minimal (3 tools: datasets, metadata, query), lacking update/delete/CRUD operations. The broader set includes many query and analysis tools, but the server's stated purpose is not fully covered.

Resources