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Openstates

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
Behavior5/5

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

The description discloses many behavioral traits beyond the annotations: requires a Pipeworx OAuth account, anonymous/BYO cannot persist, phone must be verified, SMS has a 10/day cap, and the feed is always on. These details significantly enrich the agent's understanding of side effects and constraints, and there is no contradiction with annotations.

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 front-loaded with the purpose and then details requirements, types, and delivery channels in a structured but dense manner. Every sentence contributes unique information, though it could benefit from bullet points for readability given the number of options.

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?

Given the tool's complexity (5 types, 3 delivery channels, nested params), the description is very complete: it names the supported types, explains key examples, notes the OAuth requirement, and indicates the return value. It omits webhook details and patent_grant/clinical_trial specifics, but those are covered in the schema, so the description is adequate for an agent to select and invoke the tool correctly.

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?

Although the schema has 100% parameter description coverage, the tool description adds meaning by providing concrete examples and domain context, such as explaining that items:["5.02"] indicates an officer change for sec_8k and clarifying that polymarket_edge detects cross-venue mispricings. This goes beyond the schema's terse parameter 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 creates a proactive monitoring subscription to a live-data event stream and returns the subscription id. It uses a specific verb+resource pattern and distinguishes itself from sibling tools like list_subscriptions and unsubscribe.

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 context for when to use the tool: to create persistent subscriptions with specific types and delivery channels. It mentions the OAuth account requirement and points to recent_alerts for pulling the feed, but does not explicitly contrast with list_subscriptions/unsubscribe or state 'when not to use'.

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

B3.1/5.0
Disambiguation1/5

The tool set includes multiple pairs of nearly identical tools (e.g., ask_pipeworx and ask_pipeworx_grounded, bet_research and polymarket_arbitrage) that overlap heavily in purpose. Many tools also combine unrelated functions, making it difficult for an agent to select the right one without confusion.

Naming Consistency1/5

Tool names follow no discernible pattern: snake_case (ai_visibility_check), verb_noun (ask_pipeworx, get_bill), and even lengthy descriptive names (scan_competitor_ai_presence) are mixed. The naming style is chaotic and inconsistent across the set.

Tool Count2/5

With 30 tools, the count is excessive for a server supposedly focused on OpenStates (state legislatures). Only a few tools (search_bills, get_bill, etc.) relate to the server's name, while the rest are tangentially related to data lookups, betting, or AI visibility, making the set bloated.

Completeness1/5

The server's core domain (state legislative data) is severely underserved: only about 5 tools cover bills and legislators, lacking basic CRUD operations like create, update, or delete. Many obvious operations (e.g., searching bills by subject, tracking votes) are missing, while unrelated tools dominate.

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