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Glama

Data Cincinnati

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

A4.7/5.0
Behavior5/5

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

Beyond annotations (readOnly, idempotent, non-destructive), the description details key behaviors: returning specific fields, filter support, the mark_read flag affecting subsequent calls, and that polling is acceptable. No contradictions with 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 concise with 4 sentences, each adding distinct value: purpose/output, filtering, mark_read behavior, and alternative access. No irrelevant information; front-loaded with the main action.

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 the tool's complexity (5 params, no output schema), the description sufficiently covers the purpose, output fields, filtering, mark_read effect, and polling suitability. It compensates for the missing output schema by hinting at the structure.

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% with decent descriptions for each parameter. The description adds an example filter value ('sec_8k') and reinforces the 'since' parameter as ISO timestamp, but repeats much of the schema info. The added value is modest.

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 pulls fired events from the subscription feed and returns recent alerts with specific fields (source, citation_uri, payload). It distinguishes itself from sibling tools like list_subscriptions by focusing on fired events rather than subscription metadata.

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 context on when to use the tool (pulling alerts and filtering) and mentions an alternative HTTP access method for scripts. However, it does not explicitly state when not to use the tool or compare directly with other tools.

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

A3.9/5.0
Disambiguation2/5

Several tool clusters have unclear boundaries: ask_pipeworx, ask_pipeworx_beta (explicitly identical), ask_pipeworx_grounded, and deep_research all route the same queries, and the five polymarket_* tools overlap in opportunity scanning. entity_profile, compare_entities, and recent_changes also pull similar company data, making tool selection genuinely ambiguous.

Naming Consistency4/5

All tool names are snake_case with a mostly verb-first convention (ask_pipeworx, scan_dependency, validate_claim, resolve_entity). A few noun-first names like polymarket_edges, entity_profile, and recent_alerts deviate slightly, but the pattern is predictable and readable throughout the 34-tool set.

Tool Count2/5

At 34 tools, the surface is heavy for what the server name (Data Cincinnati) implies, and only 3 tools actually relate to Cincinnati open data. The rest spans prediction markets, npm analysis, AI visibility, memory, and subscriptions, suggesting either scope creep or a misleading server name.

Completeness3/5

The research workflow is fairly covered: discovery (discover_tools, suggest_questions), query (ask_pipeworx), grounding (ask_pipeworx_grounded), verification (validate_claim), profiling (entity_profile), comparison (compare_entities), and monitoring (subscribe, recent_changes). However, notable gaps exist — no direct single-source raw query, no export/visualization, no subscription or alert management details beyond basic CRUD, and the Cincinnati-specific surface is thin (no geospatial or full-catalog access).

Resources