Skip to main content
Glama

Trove

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).

TDQS

A3.7/5.0
Behavior1/5

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

The description explains that setting mark_read=true flags events as read, which is a side effect (state modification). However, the annotations declare readOnlyHint=true, indicating no state changes. This contradiction undermines transparency. The description adds some behavioral context (e.g., return fields, polling suitability) but the annotation conflict is severe.

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: five sentences, each adding essential information. It front-loads the purpose and progressively details parameters and behavior. No wasted words or redundancy.

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?

Given the tool has 5 optional parameters, no output schema, and annotations, the description covers return fields, filtering, and polling. However, the annotation contradiction creates incompleteness: the agent cannot reliably infer whether the tool mutates state. This gap reduces completeness.

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%, so baseline is 3. The description adds examples (e.g., 'sec_8k' for type) and explains the effect of mark_read and unread_only, which goes beyond the schema descriptions. This provides moderate added semantic value.

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 'pull fired events from your subscription feed' with a specific verb and resource. It specifies the return fields (source, citation_uri, raw payload) and filtering options. Although siblings are not explicitly differentiated, the purpose is unambiguous and distinct from sibling tools like 'list_subscriptions' or 'recall'.

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 mentions that 'polls work fine' and provides an alternative REST endpoint for scripts and dashboards. It implies usage patterns through parameter descriptions (e.g., mark_read for read tracking). However, it does not explicitly contrast with sibling tools or state when not to use this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Trove toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.7/5.0
Disambiguation3/5

Several tools have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research all answer questions; entity_profile, compare_entities, recent_changes all cover company data). Descriptions provide distinctions, but an agent can easily misselect, especially between the Pipeworx query tools.

Naming Consistency2/5

Tool names follow mixed conventions: some use verb_noun (list_subscriptions, unsubscribe), others use descriptive phrases (ai_visibility_check, polymarket_arbitrage) or nouns (deep_research, entity_profile). No consistent pattern, making it harder to predict tool names.

Tool Count2/5

With 32 tools covering diverse domains (data lookups, prediction markets, memory, subscriptions), the server feels overloaded. The scope would be better served by splitting into smaller, focused servers (e.g., data query, prediction market, memory). Many tools are peripheral to a core purpose.

Completeness4/5

The tool surface is fairly comprehensive within its domains: CRUD for memory (remember/recall/forget), subscription management, extensive data query options, and prediction market analysis. Minor gaps exist (no update memory, no direct trading), but agents can work around them.

Resources