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Datagov

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

A4.5/5.0
Behavior4/5

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

The description explains the subscription creation behavior, return value, and delivery channel specifics. However, the idempotentHint annotation suggests idempotency, which contradicts the creation of multiple subscriptions with repeated calls. This is flagged as an annotation contradiction.

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 well-structured and front-loaded with the core purpose, but it is somewhat dense with many details. All sentences are informative, though conciseness could be improved by grouping related info.

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?

The description covers the tool's purpose, parameters, return value, delivery channels, requirements, and quirks (e.g., webhook signing, auto-disable). No output schema exists, but the description adequately explains the response.

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?

With 100% schema coverage, the description adds substantial value by providing concrete examples for each subscription type, explaining required fields like items for sec_8k, and detailing delivery constraints such as phone verification and SMS caps.

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 a live-data event stream and returns a subscription ID. It distinguishes itself from sibling tools 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 provides context on when to use the tool and mentions prerequisites (Pipeworx OAuth account, phone verification for SMS). It does not explicitly state when not to use it or compare to alternatives, but the usage context is clear.

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 tools have genuinely unclear boundaries: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded and deep_research both overlap with the ask_pipeworx routing layer, and scan_competitor_ai_presence is essentially ai_visibility_check for multiple entities. The descriptions are very detailed, but an agent can still easily pick the wrong member of these clusters.

Naming Consistency3/5

Names are consistently snake_case and there are useful prefix families like ask_pipeworx_*, polymarket_*, and scan_*, but the grammatical convention is mixed: noun phrases like entity_profile, deep_research, and recent_changes sit alongside imperative verbs like get_dataset, resolve_entity, and subscribe. It is readable but not a single predictable verb_noun pattern.

Tool Count2/5

34 tools is above the 25+ threshold where a tool surface starts to feel bloated, and a large subset (prediction-market analytics, memory, AI-visibility, npm scanning, llms.txt generation) is extraneous to the Data.gov identity. The core data-query story could be told with far fewer top-level tools.

Completeness4/5

The broader Pipeworx data-access workflow is well covered: discovery, routing, grounded answers, entity resolution, profiles, comparisons, claim verification, subscriptions, and memory all have dedicated tools. The Data.gov-specific piece is thin (search, metadata, organizations) but workable, with only minor gaps like no direct resource file download.

Resources