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Glama

Data Boston

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

A4.2/5.0
Behavior3/5

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

Describes return fields and mark_read mutation effect, but contradicts annotations: 'readOnlyHint=true' implies no state change, yet mark_read:true flags events as read (a write). This inconsistency reduces clarity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

First sentence states purpose. Description is clear and well-organized, though slightly verbose. Could be more concise without losing key 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?

Covers return fields, filtering, mutation behavior, and alternative access. No output schema, but description sufficiently explains what the agent receives. Appropriate for a read-oriented 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?

Adds meaning beyond schema: explains mark_read effect on future calls, filter semantics (type, since), and describes return fields. Schema coverage is 100% but description enhances 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?

Clearly states the tool pulls fired events from subscription feed, returns recent alerts with specific fields (source, citation_uri, raw payload). It's specific and unambiguous.

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?

Provides context on filtering (type, since) and the mark_read feature. Mentions polling is fine and notes an alternative access method (GET registry...), but lacks explicit when-not-to-use 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

A4.1/5.0
Disambiguation3/5

Several tools occupy adjacent territory: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share the same router, ask_pipeworx_beta is currently an exact duplicate, and there are multiple polymarket-related tools. The descriptions are unusually explicit and cross-reference when to use which, which keeps this from scoring lower.

Naming Consistency4/5

All tool names use lowercase snake_case and group into recognizable families (boston_*, pipeworx_*, polymarket_*), giving the set a consistent feel. However, the set mixes verb_noun names, bare verbs like remember/forget, and adjective_noun phrases like recent_changes, so it is not a strict verb_noun pattern throughout.

Tool Count2/5

At 34 tools this exceeds the 25+ threshold and feels bloated for a server with the narrow name "Data Boston"—only three tools directly concern Boston data, while the rest cover general research, prediction markets, subscription management, memory, AI visibility, and npm auditing. Most tools have a purpose, but the overall surface is not well-scoped.

Completeness4/5

For the broad data-access purpose, coverage is strong: routing, grounded verification, entity profiles, comparisons, change tracking, entity resolution, discovery, monitoring, memory, and Boston datasets are all represented. Minor gaps exist, such as no subscription update operation, no explicit fetch-by-citation tool, and limited boston_recent coverage, but agents can work around them.

Resources