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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

A4.1/5.0
Behavior2/5

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

Description states 'Returns the new subscription id' implying non-idempotent creation, but annotations set idempotentHint=true, creating a contradiction. It does not clarify idempotency behavior. Other behavioral traits like OAuth requirement are disclosed, but the idempotency contradiction is a significant gap.

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?

Description is informative but slightly lengthy; each sentence has purpose. Front-loaded with purpose and requirement, then examples and delivery details. Could be trimmed slightly without losing clarity.

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?

Given no output schema, description explains return value (subscription id). Covers all parameters, prerequisites, type-specific nuances, and delivery options with constraints. Sufficient for an AI agent to correctly invoke the tool.

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?

Schema coverage is 100%, but description adds extensive context: lists all type enum values with examples, explains params structure for each type, and describes delivery channels with field-level constraints (SMS requires verification, webhook signing secret). This adds significant 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?

Description clearly states 'Create a proactive monitoring subscription' with specific verb 'Create' and resource 'subscription'. It distinguishes from sibling tools like list_subscriptions, unsubscribe, and recent_alerts by focusing on creation. The supported types and delivery channels are detailed.

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?

Specifies prerequisite (Pipeworx OAuth account) and provides type-specific filter examples and delivery constraints (SMS verification, cap). Though it does not explicitly say when to avoid this tool, the context with sibling names makes it clear. Could mention listing existing subscriptions as an alternative.

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

Tools are mostly distinct but several ask_pipeworx variants and research tools overlap in purpose, which could lead to agent confusion. The presence of memory and subscription tools adds unrelated functionality.

Naming Consistency2/5

Naming is inconsistent, mixing snake_case with varying verb patterns (ask, get, search, scan, etc.) and no clear convention. Some tools have descriptive phrases (e.g., generate_llms_txt) further breaking consistency.

Tool Count2/5

34 tools is excessive for a coherent server, covering too many disparate domains (genes, data queries, betting, memory) without clear focus. A gene server should have far fewer tools.

Completeness3/5

Gene-related tools are complete for basic queries (search, get, resolve), but the server's main purpose (HGNC) is overshadowed by many unrelated Pipeworx tools, creating a mismatch. The overall surface is broad but lacks domain focus.

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