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Exa

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. Added

TDQS

A3.7/5.0
Behavior1/5

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

The description claims that setting mark_read:true flags events as read, which is a write operation, directly contradicting the readOnlyHint:true annotation. Although the description is transparent about the side effect, the contradiction downgrades the score to 1.

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 front-loaded with the main purpose, then sequentially adds parameter details, side effect, and alternative access. It is efficient but slightly verbose; could be tightened without losing clarity.

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?

Covers what is returned (source, citation_uri, payload), filter options, side effect, and alternative REST access. No output schema, so description compensates well. Lacks error handling or rate limits, but annotations cover safety.

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 covers all parameters with descriptions, but the description adds value: example type 'sec_8k', explanation of mark_read's effect on future calls, and clarification that since is ISO timestamp. With 100% schema coverage, baseline is 3, but the added context justifies a 4.

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, using specific verbs and resource ('Pull fired events'). It distinguishes from sibling tools like list_subscriptions by focusing on alerts with source, citation_uri, and payload.

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 clear context: filter by type, since, mark_read, unread_only, and mentions polling works and alternative REST endpoint. However, it does not explicitly state when not to use this tool or compare to similar tools (e.g., search).

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
Disambiguation3/5

Several tools share the same basic purpose: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route questions to data sources, and ask_pipeworx_beta is currently identical to ask_pipeworx. The polymarket_* family has five overlapping tools (edges, arbitrage, edge_tracker, fill_risk, kalshi_spread), though detailed descriptions and explicit 'use when' guidance help separate them. Overall, an agent can generally pick the right tool but faces real ambiguity in the query-router and betting clusters.

Naming Consistency4/5

Most tools follow a clear verb_noun snake_case convention (search, get_contents, resolve_entity, validate_claim, subscribe, unsubscribe). However, several noun-first names break the pattern: entity_profile, ai_visibility_check, pipeworx_feedback, pipeworx_trending, and the polymarket_* family, plus adjective-noun names like recent_alerts and recent_changes. The deviations are readable and mostly clustered around product-specific domains, so the inconsistency is minor.

Tool Count2/5

At 34 tools, this significantly exceeds the 25+ threshold for a heavy tool surface. The server bundles four distinct domains — web search, structured data routing, prediction-market analysis, and memory/subscriptions — into one MCP endpoint, which inflates the count. While each domain has some justification, a more focused split into separate servers would yield better coherence.

Completeness4/5

The surface is remarkably thorough for its blended scope: search has query/retrieve/similar/within, structured data has default/grounded/beta/deep-research modes, subscriptions have full lifecycle coverage, and memory has save/recall/delete. Minor gaps exist, such as no subscription-update tool and no direct pipeworx:// URI reader in the tool list, but these are workable. The prediction-market and entity-analysis workflows are covered end to end.

Resources