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Data Pa

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

A4.4/5.0
Behavior4/5

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

Annotations already indicate non-readOnly and non-destructive. The description adds behavioral context: OAuth requirement, type-specific parameter formats, delivery channel details including webhook signing and auto-disable, and the always-on feed. It covers most relevant behaviors without contradicting annotations.

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, front-loading the main action and then detailing types and delivery in the same paragraph. Every sentence adds value, though it could be slightly more concise without losing information.

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?

Given the tool's complexity (multiple types, delivery channels, prerequisites), the description covers all critical aspects: OAuth requirement, type-specific params, delivery options with rate limits, webhook details. It lacks error handling or subscription lifespan info, but is adequate for agent use. No output schema, but return value is stated.

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%, but the description enriches each parameter with concrete examples and constraints (e.g., sec_8k items codes, polymarket_edge topic, SMS phone verification, webhook HMAC signing). This far exceeds the schema's bare descriptions.

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's purpose: 'Create a proactive monitoring subscription to a live-data event stream. Returns the new subscription id.' It specifies the verb (create) and resource (subscription), and distinguishes it from siblings like unsubscribe and list_subscriptions.

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 explicit prerequisites: 'Requires a Pipeworx OAuth account (anonymous + BYO cannot persist subscriptions).' It also details delivery options and rate limits (SMS 10/day cap), implied usage context for each type. However, it does not explicitly contrast with alternative tools for managing 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

A3.9/5.0
Disambiguation2/5

Several clusters blur together: ask_pipeworx, ask_pipeworx_beta (explicitly identical today), and ask_pipeworx_grounded share routing, while discover_tools/suggest_questions and the Polymarket edge/arbitrage/fill-risk tools have overlapping discovery purposes. Rich descriptions reduce some confusion, but an agent must read carefully to avoid misselection.

Naming Consistency3/5

Most names are lowercase snake_case, but there is no consistent verb_noun pattern: ask_pipeworx/beta/grounded and polymarket_* are domain-prefixed, entity_profile/recent_changes are noun phrases, and query/metadata/remember are bare verbs or nouns. The naming is still readable and subfamilies share prefixes, so it is mixed rather than chaotic.

Tool Count2/5

34 tools is above the comfortable range for a single MCP server, and several could be folded together (the beta variant, visibility checks, and Polymarket scanners). The breadth reflects a large platform, but the surface feels heavy for an agent to select from confidently.

Completeness4/5

The query side is strong: PA Open Data has datasets/metadata/query coverage, and Pipeworx provides ask, deep_research, entity_profile, compare, recent_changes, validate_claim, plus subscriptions and memory with lifecycle coverage. Minor gaps: descriptions promise resolvable pipeworx:// citations but no resource/read tool is exposed, and there is no direct way to fetch an arbitrary record by citation.

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