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Data Oakland

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.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

The description discloses that the tool creates a subscription (non-readOnly), requires OAuth, and has constraints such as SMS verification and a 10/day cap. It also explains webhook signing and auto-disable after failures. Annotations are consistent (e.g., readOnlyHint=false, idempotentHint=true).

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 lengthy but well-structured: it starts with the core purpose, then details subscription types, then delivery channels. Every sentence adds value, though some users might prefer a more compact summary. The front-loaded first sentence is effective.

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?

Given the tool's complexity (3 parameters with nested objects, 5 subscription types, delivery options) and no output schema, the description thoroughly covers all aspects: inputs, prerequisites, constraints, and return value (subscription id). It leaves no obvious gaps.

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?

Although the schema already provides descriptions for all parameters, the description adds significant detail: it explains each subscription type's required/optional params (e.g., sec_8k items codes, polymarket_edge topics), delivery channel options, and constraints. This goes beyond the schema abstractions.

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 live-data event streams. It lists supported types (sec_8k, polymarket_edge, etc.) and distinguishes from siblings like list_subscriptions and unsubscribe by focusing on 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 specifies that anonymous and BYO accounts cannot persist subscriptions, requiring a Pipeworx OAuth account. It provides when-to-use context for each subscription type and delivery channel, but does not explicitly mention alternatives like list_subscriptions or recent_alerts.

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

A3.8/5.0
Disambiguation2/5

Several tool clusters have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical today, with ask_pipeworx_grounded and deep_research routing through the same 5,756-tool catalog, and validate_claim falling into the same grounded pipeline. The five polymarket_* tools plus bet_research all target prediction-market opportunities with overlapping outputs (edge_pp vs gap_pp vs spread_pp), and compare_entities/entity_profile/recent_changes share the same SEC/XBRL/news fan-out. The verbose descriptions help, but the set itself would frequently misroute an agent.

Naming Consistency3/5

All names are uniformly snake_case with no casing mixing, and the ask_pipeworx_*, polymarket_*, and pipeworx_* prefixes create recognizable families. However, the set mixes verb_noun names (validate_claim, list_subscriptions), bare verbs (query, recall, forget), and noun-phrase names (entity_profile, recent_alerts, datasets, metadata), so there is no single predictable pattern across the server.

Tool Count3/5

34 tools is heavy, but the server's scope is genuinely enormous: it is a gateway to 5,756 tools across 1,504 sources, plus prediction-market analysis, subscriptions, and memory. The count is defensible for that scope, yet several tools (generate_llms_txt, scan_dependency, ai_visibility_check, the memory trio) are peripheral to the core data mission, giving the set a scattershot feel and preventing a well-scoped rating.

Completeness4/5

The core data-research workflow is thoroughly covered: casual lookup (ask_pipeworx), grounded verification (ask_pipeworx_grounded, validate_claim), deep research (deep_research), entity resolution and profiling (resolve_entity, entity_profile, compare_entities, recent_changes), and a six-tool prediction-market suite. Subscriptions and memory have full lifecycles, and Oakland data offers search, schema, and query. Minor gaps exist — no subscription update/pause, no raw dataset export, and no write path for Oakland data — but no advertised workflow hits a dead end.

Resources