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Arcgis Albuquerque

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 goes well beyond annotations by disclosing important behavioral traits: auth requirement, account persistence limitation, phone verification, SMS daily cap, and the always-on feed behavior. It also states what the tool returns ('the new subscription id'). No contradiction with annotations; idempotentHint is not contradicted, though not explicitly explained.

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 a single dense paragraph that front-loads the purpose and then adds supporting details. Every sentence carries information, but the density might be overwhelming. It could be structured with bullet points, but it's still well-organized and does not waste words.

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?

It covers the central aspects: subscription types, params, delivery channels, return value, and account prerequisites. However, it omits the webhook delivery channel entirely from the main description (though the schema includes it), which is a slight completeness gap. Without an output schema, it does clarify the return value, so overall it is quite complete.

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?

Schema coverage is 100%, but the description adds meaningful examples for each subscription type, such as items:['5.02'] meaning officer change, and clarifies the structure of `params` with concrete JSON snippets. It also explains delivery channel requirements (e.g., phone must be verified). This provides value 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?

The description opens with a specific verb+resource: 'Create a proactive monitoring subscription to a live-data event stream.' It clearly identifies the tool's function and differentiates it from siblings like list_subscriptions and unsubscribe by using the verb 'Create' and naming subscription types.

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?

It clearly states the context: requires a Pipeworx OAuth account, and explicitly notes that anonymous + BYO cannot persist subscriptions. It also mentions the always-on feed and alternatives like recent_alerts or a direct JSON endpoint. However, it does not explicitly say when not to use this tool (e.g., for one-off queries) or compare with other alerting tools.

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

A3.6/5.0
Disambiguation2/5

Several tool clusters overlap heavily: ask_pipeworx and ask_pipeworx_beta are documented as functionally identical right now, and polymarket_arbitrage / polymarket_edges / polymarket_edge_tracker all surface trade opportunities with similar outputs. discover_tools and suggest_questions also cover similar 'what can I do' territory.

Naming Consistency3/5

All names are snake_case and many use verb_noun (query_layer, resolve_entity, generate_llms_txt), but a large minority use noun/adjective phrases (layer_info, recent_changes, polymarket_edges, bet_research) or bare verbs (remember, forget, recall). The pattern is readable but not fully predictable.

Tool Count2/5

34 tools is over the 25-tool threshold and deeply mismatched with the server name: only 3 of them (search_datasets, query_layer, layer_info) relate to ArcGIS Albuquerque. Most of the surface is a general-purpose Pipeworx data platform, making the set bloated and unfocused.

Completeness2/5

The advertised ArcGIS domain has only search-schema-query coverage: there is no way to list all datasets, apply spatial filters, or get service-level metadata. The Pipeworx half is feature-rich, but for the server as titled the tool surface has significant gaps and a large amount of irrelevant functionality.

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