Skip to main content
Glama

Scrapingdog

by io.github.pipeworx-io

Validate Claim

validate_claim
Read-onlyIdempotent

"Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported / could_not_verify), the grounded or structured actual value with pipeworx:// citation, and reasoning. IMPORTANT for callers: could_not_verify means the check did not happen (our LLM or source failed) and carries verification_error{stage,detail} — it is NOT evidence for or against the claim, and must not be shown as one. unsupported means we looked and cover no source for it. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
claimYesNatural-language factual claim, e.g., "Apple's FY2024 revenue was $400 billion" or "Microsoft made about $100B in profit last year".
tolerance_pctNoMax percent deviation still graded approximately_correct (0.5–50). Overrides the tolerance implied by the claim wording — set 1–2 for hallucination detection where any material error must be refuted. Default: implied by wording, capped at 5.

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

The annotations already mark this as read-only and idempotent, and the description adds valuable behavioral context: it explains the two execution paths, the exact verdict list, that could_not_verify indicates a failure not evidence, and that unsupported means no source coverage. This goes well beyond the safety hints.

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 information-dense, with examples front-loaded and an important callout for could_not_verify. Every sentence contributes, though it could be trimmed slightly without losing essential details.

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?

Without an output schema, the description carries the responsibility of explaining return values, and it does so thoroughly: lists all verdicts, mentions the actual value with citation and reasoning, and clarifies the meaning of could_not_verify and unsupported. It also covers the two routing paths, making it complete for a complex tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the description does not need to explain the parameters. It adds minor context, such as 'exact percent-delta math' for financial claims which relates to tolerance_pct, but does not provide additional meaning beyond the schema's own detailed 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 identifies the tool as a natural-language claim verification tool with explicit query phrases ('fact check', 'verify the claim that…'), and describes two distinct pipelines (SEC EDGAR for financial claims, grounded pipeline for others), which differentiates it from generic Q&A tools like ask_pipeworx.

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?

It provides an explicit usage instruction: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also distinguishes between financial and non-financial claims, and notes it replaces 4–6 sequential calls, but does not name an alternative tool to use instead, so usage guidance is clear but lacks explicit 'when not to use'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Scrapingdog toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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