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Scrapingdog

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

A4.6/5.0
Behavior5/5

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

The description discloses behavioral traits beyond annotations: idempotency is implied by annotations, but the description adds specifics like the OAuth requirement, the fact that subscriptions persist, delivery channel limits (SMS cap), and webhook HMAC signing and auto-disable. There is no contradiction with 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 long but well-structured, starting with the core purpose, then requirements, then types with examples, then delivery channels. Every sentence adds value, though it could be slightly more compact without sacrificing clarity. The front-loading of the purpose is effective.

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?

For a tool with no output schema, the description adequately covers the return value (subscription id), prerequisites, type-specific parameters, and delivery options. It could mention the full response structure (e.g., whether it includes the subscription ID in a specific field), but overall it is sufficient for correct invocation.

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 goes far beyond the schema by providing real-world examples for each type (e.g., sec_8k with items:['5.02']), explaining the params object structure, and detailing the delivery object constraints (e.g., phone must be verified, webhook signing secret). This significantly enhances 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') and the resource ('live-data event stream'). It also specifies the return value ('Returns the new subscription id'), and the examples of subscription types (sec_8k, polymarket_edge, fred_series) distinguish it from sibling tools like list_subscriptions or unsubscribe.

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 usage context: 'Requires a Pipeworx OAuth account (anonymous + BYO cannot persist subscriptions)'. It lists supported types and their parameters, and mentions delivery channels with constraints (e.g., SMS 10/day cap). While it doesn't explicitly say when not to use, the prerequisites and details effectively guide the agent on when this tool is appropriate.

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

A3.7/5.0
Disambiguation2/5

Multiple tools have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical, ai_visibility_check overlaps with scan_competitor_ai_presence, and the six Polymarket tools all orbit the same edge-detection concept. Descriptions are detailed, but an agent would frequently have to read long text to decide which near-overlapping tool to call.

Naming Consistency2/5

Naming is mostly snake_case but semantically inconsistent: some names are verb-led (ask_pipeworx, validate_claim, remember), some noun-led (polymarket_edges, entity_profile), and some use a vendor prefix (scrapingdog_scrape, scrapingdog_amazon_product). The polymarket_edges vs polymarket_edge_tracker singular/plural pairing adds further confusion.

Tool Count2/5

34 tools is above the 25+ threshold and the count is not justified by a single clear purpose. The server is named Scrapingdog but most tools are unrelated Pipeworx research, memory, subscription, and prediction-market functionality, making the set feel overstuffed and unfocused.

Completeness3/5

The data-research and subscription/memory lifecycles are fairly complete, with create/read/delete coverage for those areas. However, relative to the Scrapingdog scraping identity, the surface is thin: only three scraping tools exist, and there is no direct way to fetch a Pipeworx record by URI or manage scraped-data artifacts.

Resources