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Markdown

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

A3.8/5.0
Behavior1/5

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

The description states that setting mark_read:true will 'flag returned events read,' which is a state-modifying side effect. However, the annotations declare readOnlyHint:true, implying the tool is read-only. This is a direct contradiction between the description and the annotations, so transparency must be scored as 1.

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 succinct and well-structured: it opens with the primary purpose, then describes the return format, filtering, mark_read semantics, and polling. Each sentence adds useful information without redundancy or fluff.

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?

Without an output schema, the description carries the burden of explaining return values, which it does (source, citation_uri, raw payload). It also covers filtering, mark_read usage, and polling. It does not mention potential interactions between unread_only and mark_read, but that is a minor gap given the schema descriptions. The contradiction with annotations is a transparency issue, not a completeness one.

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?

The input schema already has 100% coverage for all parameters, so the baseline is 3. The description adds value beyond the schema by giving a concrete example for 'type' (e.g., "sec_8k"), explaining that 'since' is an ISO timestamp, and detailing the behavioral effect of mark_read on subsequent calls. This additional context justifies a score of 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 the tool's purpose: 'Pull fired events from your subscription feed.' This uses a specific verb ('Pull') and identifies the resource ('fired events from your subscription feed'), and adds detail about event contents (source, citation_uri, raw payload). It distinguishes itself from sibling tools like list_subscriptions and recent_changes by focusing on fed events.

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 provides clear usage context: it explains filtering by type and/or since, the effect of mark_read on subsequent calls, and that polling is fine. It also mentions an alternative endpoint for scripts/dashboards. However, it does not explicitly name sibling tools as alternatives or provide exclusion criteria, so it falls short of a 5.

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
Disambiguation2/5

Several tool clusters are hard to distinguish: the four ask_pipeworx variants overlap heavily (beta currently behaves identically to ask_pipeworx, and grounded shares routing), and the half-dozen prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) have fuzzy boundaries. Memory and markdown utilities are clear, but the overlapping data-query and prediction-market clusters create real misselection risk.

Naming Consistency4/5

Names are almost uniformly snake_case and mostly verb-first (ask_, compare_, extract_, scan_, validate_, remember, forget), which is predictable. Minor deviations like entity_profile, recent_changes, and polymarket_edges are noun-first but still follow the same lowercase_snake pattern, so no chaotic mixing of conventions exists.

Tool Count2/5

34 tools is heavy for a server seemingly named 'Markdown', especially when the majority are actually Pipeworx data-research and prediction-market tools. Several tools duplicate or wrap each other (ask_pipeworx_beta, scan_competitor_ai_presence, bet_research et al.), so the count feels bloated; it is not as extreme as 50+, but it exceeds a well-scoped set.

Completeness4/5

Within the actual dominant domain — structured data research plus prediction markets — the surface is broad: routing queries, grounded answers, deep research, entity profiles, comparisons, resolution, claim validation, semantic search, discoverability, subscriptions/alerts, and memory are all covered. Minor gaps exist (e.g. no direct pipeworx:// citation-fetch tool, and markdown support is thin), but the core workflows have no dead ends.

Resources