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Data Bloomington

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 reveals that setting mark_read:true modifies state (flags events as read), which contradicts the annotation readOnlyHint=true. This is a direct contradiction between the described behavior and the annotation hint.

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?

The description is concise (four sentences) and well-structured, starting with the core purpose. It could be slightly shorter but remains informative without unnecessary detail.

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

Completeness4/5

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

Given the optional parameters and no output schema, the description adequately explains return fields and provides usage notes (polls, alternative access). It is complete enough for an agent to use the tool effectively.

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?

All five parameters are described in the schema (100% coverage). The description adds value by explaining the mark_read effect and providing example filter values (e.g., 'sec_8k'). This goes beyond the schema's basic descriptions.

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 return fields (source, citation_uri, payload) and filtering options, distinguishing it from potential siblings by focusing on alert retrieval from a persisted 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 explains filtering by type and since, and how mark_read works to manage read state. It also mentions that 'Polls work fine' and provides an alternative URL for scripts/dashboards, offering usage context. However, it does not explicitly state when not to use this tool versus siblings.

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

A3.7/5.0
Disambiguation2/5

Multiple tool clusters overlap heavily — ask_pipeworx_beta is functionally identical to ask_pipeworx right now, and the six Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research, etc.) have blurry boundaries. The entity-research family (entity_profar, compare_entities, recent_changes) is also easy to misselect despite detailed descriptions.

Naming Consistency3/5

All names are snake_case, but conventions are mixed: verb_noun (resolve_entaty, validate_claim), bare verbs (remember, recall, query), adjective_noun (recent_alerts, recent_changes), and brand-prefixed nouns (polymarket_edges, pipeworx_trending). Family prefixes and verbs help readability, but the overall pattern is not uniform.

Tool Count2/5

At 34 tools this exceeds the 25+ threshold and spans loosely related domains — Bloomington open data, Pipeworx research, prediction markets, AI marketing, memory, npm scanning, and llms.txt generation. The scope feels heavy and unfocused relative to the 'Data Bloomington' server name.

Completeness4/5

The core research lifecycle is well covered: routing (ask_pipeworx), grounded verification, deep research, entity profiles/comparisons/changes, claim validation, entity resolution, subscriptions, and memory all exist. Minor gaps remain — no standalone tool for fetching a pipeworx:// citation URI and no API-key management — but agents can work around them.

Resources