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Expression Atlas

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

A5/5.0
Behavior5/5

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

The description goes beyond annotations by detailing auth requirements, channel-specific behaviors (webhook HMAC signing and auto-disable), and limits like SMS daily cap. It does not contradict annotations and adds significant safety and behavioral context.

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 well-structured and front-loaded: purpose first, then requirements, then types, then delivery details. Every sentence adds valuable information without redundancy, making it concise given the tool's complexity.

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?

Despite no output schema, the description covers return value (subscription ID), all necessary prerequisites, type-specific parameter details, and delivery options with behavioral notes. It is comprehensive for a subscription creation 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 the description adds concrete examples for each subscription type and elaborates on delivery field details (e.g., webhook signing secret). This enhances understanding beyond the schema's bare descriptions.

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: 'Create a proactive monitoring subscription to a live-data event stream.' It specifies that it returns a subscription ID and lists supported types with examples, distinguishing it from siblings like 'unsubscribe' and 'list_subscriptions'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use guidance, including prerequisite of a Pipeworx OAuth account, and explains delivery channels and their limitations (e.g., SMS cap of 10/day, webhook auto-disable). It contrasts with siblings by implying that this tool is for creation, not removal or listing.

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

A3.7/5.0
Disambiguation3/5

Many tools have overlapping purposes (e.g., multiple Polymarket tools, multiple company information tools), but descriptions help distinguish them. However, the variety of domains (brand monitoring, betting, package scanning, memory, etc.) can confuse an agent.

Naming Consistency2/5

Tool names have no consistent naming pattern: some are verb_noun (validate_claim), some are noun_verb (bet_research), some are compound (ai_visibility_check), and some are single word (forget). This makes it hard to predict tool names.

Tool Count2/5

28 tools is high, and the set spans many unrelated domains (brand visibility, SEC/FDA data, Polymarket, npm packages, memory, etc.) while the server is named 'Expression Atlas' with only 2 tools related to that purpose. The tool count feels excessive and unfocused.

Completeness1/5

For a server named 'Expression Atlas', only two tools (get_experiment, search_experiments) cover the domain. There are no tools for submitting, updating, or deleting experiments, nor any for data visualization. The surface is severely incomplete.

Resources