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Glama

Maptiler

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

A3.9/5.0
Behavior4/5

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

Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds behavioral context such as the ability to mark events read with mark_read, which affects subsequent calls. It also discloses the return fields and that polling is acceptable, extending beyond the 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 extremely concise (4 sentences) with the main purpose upfront. Every sentence adds value—return fields, filtering, mark_read behavior, and alternative access method—without superfluous words.

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 5 optional parameters, no output schema, and comprehensive annotations, the description covers key aspects: what is returned, how to filter, mark_read semantics, and polling suitability. It is nearly complete; a minor gap is no mention of pagination or handling beyond the limit parameter.

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 descriptions for all 5 parameters. The description enhances this with concrete examples (e.g., type 'sec_8k'), clarifies the since parameter format (ISO timestamp), and explains the effect of mark_read. This adds meaningful context beyond the schema definitions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

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 a subscription feed and specifies what fields are returned (source, citation_uri, raw payload). It also mentions filtering by type and since. However, it does not explicitly differentiate from sibling tools like list_subscriptions or search_within.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description indicates that polling works fine and provides an alternative HTTP endpoint for scripts/dashboards, suggesting a secondary use case. However, it does not provide explicit guidance on when to use this tool versus other alert-related siblings (e.g., subscribe, list_subscriptions).

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

B3.4/5.0
Disambiguation3/5

Many tools have distinct purposes, but there is overlap, e.g., ask_pipeworx vs ask_pipeworx_grounded vs deep_research all for data lookup, and multiple Polymarket tools. Descriptions are detailed enough to distinguish, but the set is confusing.

Naming Consistency2/5

Naming is highly inconsistent: snake_case (ai_visibility_check), verb_noun (resolve_entity), underscores (ask_pipeworx), and domain-specific prefixes (polymarket_). No consistent pattern across tools.

Tool Count2/5

With 38 tools spanning geospatial, data lookup, prediction markets, memory, feedback, etc., the count is too high for a coherent server. Many tools are one-off and unrelated to the server's name (Maptiler).

Completeness2/5

The tool set covers many areas but lacks obvious gaps for each domain (e.g., MapTiler only has 4 tools). The overall surface is a collection of unrelated features, not a cohesive domain, so completeness is poor for any single purpose.

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