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by io.github.pipeworx-io

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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

A4.5/5.0
Behavior4/5

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

The description discloses behavioral traits beyond annotations, such as the requirement for an OAuth account, SMS cap of 10/day, webhook auto-disabling, and HMAC signing. It matches the annotations (readOnlyHint false, openWorldHint true, etc.) without contradiction.

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 well-structured, starting with the primary action and then detailing types, parameters, and delivery options. It is slightly long but every sentence adds value; could be trimmed slightly for better conciseness.

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?

The description covers all necessary context: required authentication, supported event types with examples, parameter details, delivery channels with limitations, and return value (new subscription id). It is complete given the tool's complexity and the absence of an output schema.

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

Parameters5/5

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

The description adds significant meaning beyond the schema, providing examples and explanations for each parameter value (e.g., sec_8k items codes, polymarket_edge topic, delivery channel caveats). Schema coverage is 100%, but the description enriches understanding.

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 creates a proactive monitoring subscription to a live-data event stream, which is a specific action on a distinct resource. It distinguishes itself from sibling tools like unsubscribe, list_subscriptions, and 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 provides explicit context for usage, including the requirement for a Pipeworx OAuth account and supported subscription types and delivery channels. However, it does not explicitly state when not to use or compare directly with siblings.

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 clusters of tools are hard to tell apart: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve overlapping query paths, and the five polymarket_* tools plus bet_research cover heavily overlapping prediction-market analysis. Some pairs are nearly identical in purpose, like ai_visibility_check vs scan_competitor_ai_presence, and the descriptions must be read closely to avoid misselection.

Naming Consistency2/5

All names are snake_case, but the naming style is highly inconsistent across the set: some use verb_noun (ask_pipeworx, generate_llms_txt, resolve_entity), some are noun phrases (entity_profile, pipeworx_feedback, recent_alerts), and some use a vendor prefix without a clear verb (polymarket_edges, polymarket_edge_tracker). The pattern shifts between domain-specific prefixes (polymarket_*, pipeworx_*) and generic verbs with no predictable rule.

Tool Count2/5

32 tools is too many for a cohesive server, especially when the surface sprawls across unrelated domains: data querying, prediction markets, memory, subscriptions, npm scanning, AI visibility checks, and llms.txt generation. Many tools could be consolidated (the ask_pipeworx family, the polymarket family, the entity-comparison family), which would make the count feel more justified.

Completeness4/5

Within its apparent purpose as a broad data-and-research assistant, the tool set is fairly complete: it covers entity resolution, lookup, grounded verification, deep research, comparisons, memory CRUD, subscription lifecycle, discovery, and feedback. Minor gaps exist, such as no direct tool to fetch a record by its pipeworx:// citation URI (search_within implies fetching happens elsewhere) and no evident update operation for stored memories beyond save/delete.

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