Skip to main content
Glama

Subscribe to Alerts

subscribe
Idempotent

Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id. Requires a Pipeworx OAuth account (anonymous + BYO cannot persist subscriptions). Supported types: "sec_8k" (8-K filings matching ticker + item codes — e.g. items:["5.02"] = officer change), "polymarket_edge" (Polymarket↔Kalshi cross-venue mispricings — params:{topic:"fed"}), "fred_series" (new FRED observations — params:{series_id:"UNRATE"}). Delivery channels: feed (always on — pull via recent_alerts or GET registry.pipeworx.io/alerts.json), and optionally email (set delivery:{email:"you@x.com"}) or sms (delivery:{sms:"+15551234567"} — phone must be verified at /account first; 10/day cap).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesSubscription type.
paramsYesType-specific filter. sec_8k: {ticker:"AAPL", items?:["5.02","1.01"]}. polymarket_edge: {topic:"fed", min_spread_bps?:500}. fred_series: {series_id:"UNRATE"}. patent_grant: {applicant:"Apple Inc."}. clinical_trial: {sponsor?:"Pfizer", condition?:"lung cancer", phase?:"PHASE3"} (sponsor or condition required).
deliveryNoOptional delivery channels in addition to the always-on persistent feed. {email:"you@x.com"} sends a templated alert per fired event. {sms:"+15551234567"} sends an SMS per event — must match the verified phone on the caller's account (verify at https://pipeworx.io/account first; 10/day cap). {webhook:"https://..."} POSTs each event JSON to your endpoint, HMAC-signed — the response includes delivery.webhook_secret (whsec_…) ONCE; verify X-Pipeworx-Signature = sha256 HMAC of "<X-Pipeworx-Timestamp>.<raw body>". Auto-disabled after 10 consecutive failing runs.

TDQS

A4.3/5.0
Behavior5/5

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

Beyond annotations, the description discloses important behavioral details: the need for an OAuth account, persistence limitations, the always-on feed, email/SMS requirements (verified phone, 10/day cap), and the fact that the subscription id is returned. This adds significant context that annotations alone do not provide.

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 long but densely packed with necessary information: purpose, prerequisites, supported types with examples, and delivery channels. It is well-structured and front-loaded with the main purpose, though the length could be trimmed without losing value.

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?

For a complex tool with 5 subscription types and nested delivery options, the description covers purpose, prerequisites, examples, and return value. It omits the webhook delivery channel (though the schema covers it) and only highlights three of the five types, but this is a minor gap given the schema's completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already provides 100% parameter descriptions with type-specific examples, and the description largely mirrors these examples (e.g., items:['5.02'], series_id:'UNRATE'). It does not add new semantic meaning beyond the schema, earning the baseline score of 3.

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 function: 'Create a proactive monitoring subscription to a live-data event stream.' This specific verb+resource phrasing distinguishes it from siblings like 'list_subscriptions' and 'unsubscribe', and the mention of 'proactive monitoring' contrasts with pull-based tools like 'recent_alerts'.

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 gives explicit prerequisites (requires Pipeworx OAuth account, anonymous and BYO cannot persist) and explains delivery channels including the always-on feed that can be pulled via recent_alerts. It does not explicitly state when not to use this tool, but the context and comparisons to alternatives are clear enough.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.5/5.0
Disambiguation2/5

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all answer research questions, and the Kitsu-specific tools are mixed with unrelated Polymarket, memory, and utility tools. Despite detailed descriptions, the boundaries between many tools are unclear.

Naming Consistency2/5

The server mixes single-word nouns (anime, manga, categories), verb_noun pairs (search_anime, top_anime), verb phrases (ask_pipeworx, generate_llms_txt), and domain-prefixed families (polymarket_*, pipeworx_*) with no consistent overall convention. While some sub-families are internally consistent, the set as a whole lacks a predictable pattern.

Tool Count2/5

38 tools is excessive for a server ostensibly about Kitsu anime/manga, with only 7 tools actually serving that domain. The rest are unrelated (Pipeworx research, Polymarket trading, memory, subscriptions), making the set bloated and unfocused for its stated purpose.

Completeness2/5

The Kitsu domain lacks common operations like filtered search, character/episode data, or user lists. Meanwhile, the Pipeworx/prediction-market tools form an arbitrary subset of their domains (e.g., no general Kalshi data, no SEC full-text search), so the overall surface is incomplete for any single purpose.