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Arcgis Branson

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?

Beyond the annotations, the description discloses meaningful behavioral traits: the OAuth account requirement and persistence limitation for anonymous/BYO users, phone verification for SMS, the 10/day cap, and the always-on feed behavior. It also notes the returned subscription id. There is no contradiction with annotations (readOnlyHint=false, idempotentHint=true, destructiveHint=false).

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 dense and front-loaded with the main purpose, then systematically covers supported types and delivery channels. It is a single long paragraph but every clause contributes useful information. A bit of restructuring could improve scannability, but it is not excessively verbose.

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?

The description covers the purpose, prerequisites, subscription types, delivery options, and return value. It omits the webhook delivery channel and two subscription types from the main text, but the schema fully documents those. Overall, the description is sufficient for an agent to invoke the tool correctly when combined with structured schema data.

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?

With 100% schema description coverage, the baseline is 3. The description adds extra semantic value for sec_8k (e.g., items:["5.02"] = officer change), polymarket_edge (topic:"fed"), and fred_series (series_id:"UNRATE"). However, it omits patent_grant and clinical_trial from its examples, though those are covered in 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 clearly states the action ('Create a proactive monitoring subscription') and the resource ('live-data event stream'), and it explicitly mentions the return value (subscription id). It also distinguishes itself from sibling tools like list_subscriptions and unsubscribe by emphasizing the creation aspect and the persistent feed.

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 strong usage context: it requires a Pipeworx OAuth account, lists supported subscription types with examples, and explains delivery channels including how to pull from the always-on feed. It does not explicitly name alternatives or exclusions, but the use case and prerequisites are clear enough for an agent to select this tool appropriately.

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

A3.9/5.0
Disambiguation5/5

All 33 tools have clearly distinct purposes, even those that seem related like ask_pipeworx, ask_pipeworx_grounded, and deep_research are well-differentiated by use case and behavior. Memory tools (remember/recall/forget) and subscription tools (subscribe/unsubscribe/list_subscriptions/recent_alerts) are similarly distinct.

Naming Consistency3/5

All tools use snake_case, but naming patterns are mixed: some are single verbs (forget, recall), some verb_noun (query_layer, search_datasets), some noun_noun (entity_profile, layer_info), and some longer phrases (polymarket_kalshi_spread, scan_competitor_ai_presence). While readable, the inconsistency makes the set feel less coherent.

Tool Count2/5

With 33 tools, the surface is overly broad for a server named after a specific ArcGIS dataset. Many tools are unrelated to the core purpose (e.g., polymarket tools, npm scanning, AI visibility), making the count feel bloated and unfocused.

Completeness2/5

For the stated ArcGIS Branson focus, only 3 tools (search_datasets, query_layer, layer_info) are relevant, offering only read access. The rest are a miscellaneous collection from the Pipeworx ecosystem and other domains, leaving obvious gaps in GIS functionality and no write or analysis capabilities.

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