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Glama

Kegg

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

A4.5/5.0
Behavior4/5

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

Adds behavioral details beyond annotations: mark_read effect, returned fields (source, citation_uri, payload). No contradiction with readOnlyHint or destructiveHint.

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?

Four focused sentences, no fluff, logical flow: purpose, return fields, filtering, polling. Highly concise yet informative.

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

Completeness5/5

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

Covers return format, filtering, read-state management, and alternative access. No output schema needed; description adequately explains what to expect.

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%, but description adds examples (sec_8k), format (ISO timestamp), and mark_read behavior. Adds value beyond 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?

Clearly states 'Pull fired events from your subscription feed' with specific verb and resource. Distinguishes from sibling tools like list_subscriptions, subscribe, etc.

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 filtering guidance (type, since) and polling advice. Mentions alternative endpoint for scripts/dashboards, but lacks explicit when-not-to-use comparisons.

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

A3.8/5.0
Disambiguation3/5

Most tools have clearly described distinct purposes, but several overlap: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, and ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all occupy neighboring query/discovery territory. The Polymarket and memory tool families, by contrast, are well differentiated.

Naming Consistency3/5

All names are snake_case and readable, but the set mixes verb-first names (ask_pipeworx, compare_entities, resolve_entity, validate_claim) with noun-phrase names (entity_profile, bet_research, recent_alerts, polymarket_arbitrage) and brand prefixes (pipeworx_*, polymarket_*). There is no consistent verb_noun pattern across the toolkit.

Tool Count2/5

34 tools is well beyond the heavy range, and the count is especially inappropriate because the server is named Kegg but only find, get_entry, and list_database relate to KEGG bioinformatics. The remaining 31 tools span unrelated domains (generic data research, prediction markets, memory, subscriptions, AI visibility, npm scanning), making the scope feel like several products merged into one.

Completeness2/5

As a KEGG server, the surface is severely thin: three read-only tools with no pathway mapping, sequence search, or cross-reference utilities. The Pipeworx research and Polymarket betting subsystems are more complete, but their presence under a Kegg server makes the overall surface incoherent rather than complete.

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