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Data Montreal

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

A4.7/5.0
Behavior5/5

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

Adds significant behavioral details beyond annotations: phone verification for SMS, 10/day cap, webhook signing secret returned once, auto-disable on failure. Annotations already indicate non-read, non-destructive, and idempotent, and description aligns with these.

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?

Well-structured with core purpose upfront. Contains necessary detail but could be slightly more concise by separating type-specific notes. Overall efficient.

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?

Completely covers prerequisites, types, delivery options, constraints, and return value. No output schema, but return is mentioned. For a complex tool with nested objects, this is fully adequate.

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% and description adds rich examples and constraints for each parameter, especially for params (type-specific filters) and delivery (detail on SMS, email, webhook). Enhances understanding beyond the 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 the tool creates a proactive monitoring subscription and returns a subscription id, using specific verbs and resource. It 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?

Provides clear context on required account type (Pipeworx OAuth), supported types, and delivery channels with constraints. Does not explicitly state when not to use or alternatives, but the context is sufficient for an AI.

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

A4/5.0
Disambiguation3/5

Most tools have distinct purposes with detailed descriptions, but ask_pipeworx_beta is explicitly identical to ask_pipeworx, and the several polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage) have overlapping prediction-market territory. Descriptions help differentiate, but the overlaps could still cause misselection.

Naming Consistency4/5

The majority follow a clear verb_noun snake_case pattern (e.g., compare_entities, resolve_entity, generate_llms_txt). A few tools deviate with noun/adjective prefixes (montreal_datasets, montreal_recent, pipeworx_feedback, recent_alerts) or single verbs (remember, recall, forget), but the overall style is consistent and readable.

Tool Count2/5

At 34 tools, the set exceeds the 25+ threshold and feels heavy. While each tool has a distinct role, the sheer number—spanning data access, prediction markets, memory, subscriptions, and meta-tools—makes the surface harder for agents to navigate compared to a more focused server.

Completeness4/5

For its broad data-gateway purpose, the server covers a wide range: lookups, research, entity resolution, claim validation, memory, subscriptions, and feedback. The Montreal-specific subset (datasets, query, recent) is adequate for the apparent scope, with only minor gaps like no subscription-update tool.

Resources