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Glama

Data Mesa

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

B3.4/5.0
Behavior1/5

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

Description contradicts annotations: readOnlyHint=true in annotations suggests no state change, but description says mark_read:true flags events as read, modifying state. Also, idempotentHint is violated since subsequent calls may differ. This is an annotation contradiction.

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 a single, well-structured paragraph that front-loads the purpose and efficiently covers return fields, filtering, side effects, and alternatives. Slightly dense but every sentence adds value.

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?

Provides return field info, filtering, mark_read behavior, and alternative access. However, the contradiction between description and annotations undercuts completeness, and some details like format of source are omitted. Adequate but not fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. Description adds minor extra context (e.g., example type 'sec_8k', effect of mark_read) but does not significantly enhance 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 it pulls fired events from the subscription feed and returns recent alerts with specific fields. It is highly specific with verb 'Pull' and resource, and does not need to distinguish from siblings given the clear purpose.

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 explicit filtering options (type, since) and explains the mark_read flag behavior. Mentions polling and an alternative HTTP endpoint. Lacks explicit when-not-to-use or comparisons to sibling tools, but offers solid context.

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
Disambiguation3/5

Several tool groups have overlapping purposes (e.g., ask_pipeworx variants, polymarket research tools, visibility checks), despite detailed descriptions. An agent may struggle to choose between closely related options.

Naming Consistency2/5

Tool names mix snake_case verb_noun patterns (e.g., ai_visibility_check, compare_entities) with noun-heavy compound names (e.g., polymarket_arbitrage, deep_research) and single-word names (e.g., query, recall). No consistent convention.

Tool Count2/5

With 34 tools, the surface is heavy for a research/data server. Many tools are variants of core capabilities (ask_pipeworx, polymarket edges), suggesting consolidation would improve usability.

Completeness3/5

The server covers a broad range: data research, prediction markets, entity profiles, monitoring. However, the abundance of specialized variants and gaps in unified workflows (e.g., needing separate tools for simple vs grounded queries) reduce completeness.

Resources