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Arcgis Peoria

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

A4/5.0
Behavior3/5

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

With annotations present (readOnlyHint: false, idempotentHint: true, destructiveHint: false), the description adds useful context: OAuth account requirement, feed always-on, SMS verification and 10/day cap. However, it uses the phrase 'new subscription id' which could be misinterpreted as non-idempotent, creating ambiguity with the idempotentHint annotation. No outright contradiction, but idempotency is not 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 dense but structured: purpose, return value, auth requirement, supported types, and delivery channels. Each sentence provides necessary information without fluff. It is longer than minimal but justified given the variety of options.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers auth, return value, and several delivery channels, but omits the webhook delivery channel (present in schema) and two subscription types (patent_grant, clinical_trial). Since these are valid options, the description is incomplete. Output schema is absent, but the tool returns an id, which is explained.

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 description coverage is 100%, so the baseline is 3. The description adds value by providing concrete examples for parameters (e.g., items: ["5.02"] meaning officer change) and clarifying delivery options like email and SMS. The webhook parameter is only in the schema, but the description still enriches the understanding.

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 to a live-data event stream' and mentions 'Returns the new subscription id.' It distinguishes from sibling tools like list_subscriptions, unsubscribe, and recent_alerts by focusing on subscription creation.

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 gives clear context for when to use the tool: for proactive monitoring of specific event types. It lists supported types and delivery channels. However, it does not explicitly say when not to use it (e.g., for one-time data queries) or direct users to alternatives like list_subscriptions for viewing existing subscriptions.

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

A3.5/5.0
Disambiguation2/5

Many tools are very similar (e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and serve the same purpose with slight variations, making it hard for an agent to choose correctly. Additionally, the tool set mixes completely unrelated domains (ArcGIS geospatial vs. Pipeworx/Polymarket data), further confusing the purpose of each tool.

Naming Consistency2/5

Tool names follow multiple conventions: ask_pipeworx uses snake_case, while layer_info and query_layer use snake_case as well but with a different pattern. There is no consistent verb_noun pattern across the set; some are descriptive (validate_claim) while others are vague (process, run). The mix of conventions and lack of a unified naming scheme hurts predictability.

Tool Count1/5

At 34 tools, the count is excessive for a server supposedly focused on ArcGIS Peoria. Only 3 tools (layer_info, query_layer, search_datasets) are actually related to geospatial data, while the other 31 are from external services (Pipeworx, Polymarket). This mismatch suggests the server is extremely poorly scoped.

Completeness1/5

For a geospatial server, the tool set is severely incomplete. It lacks basic GIS operations like spatial filtering, editing, or analysis. The three geospatial tools only provide schema discovery and simple attribute queries. Meanwhile, the bulk of the tools cover a completely different domain (data lookup, prediction markets), leaving the core domain almost entirely unaddressed.

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