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Google_search_console

by io.github.pipeworx-io

Compare Entities

compare_entities
Read-onlyIdempotent

"Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valuesYesFor company: 2–5 tickers/CIKs (e.g., ["AAPL","MSFT"]). For drug: 2–5 names (e.g., ["ozempic","mounjaro"]).

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint. The description adds behavioral details: data sources (SEC EDGAR/XBRL, FAERS), sorting by primary metric, and the efficiency claim of replacing 8–15 sequential lookups, which is useful beyond 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 a single dense paragraph that front-loads query examples and key instructions. While not extremely concise, every sentence adds value; could be slightly more structured but effective overall.

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 2 parameters and no output schema, the description adequately covers data sources, sorting behavior, citation URIs, and performance benefit. It provides sufficient context for correct invocation without missing critical details.

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

Parameters4/5

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

Schema coverage is 100% (both parameters described). The description enriches understanding by giving concrete examples for 'type' (what data each pulls) and 'values' (tickers/CIKs for company, drug names), adding practical guidance beyond schema.

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: 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call.' It specifies distinct behaviors for company vs. drug types and contrasts with sequential single-pack lookups, making it unambiguous and differentiated from siblings.

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 explicitly recommends preference over sequential lookups ('ALWAYS PREFER over sequential single-pack lookups') and provides example queries. It lacks explicit when-not-to-use scenarios but clearly defines comparison context.

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

A3.5/5.0
Disambiguation2/5

The set has several overlapping clusters: ask_pipeworx and ask_pipeworx_beta are explicitly identical, multiple polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) occupy the same prediction-market space, and ai_visibility_check/scan_competitor_ai_presence are near-duplicates. A few tools (memory triad, gsc_* calls) are crisp, but the boundaries between the meta-research tools (ask_pipeworx, deep_research, discover_tools, validate_claim) are not obvious enough to prevent misselection.

Naming Consistency2/5

Naming is a mixed bag: some tools follow snake_case verb_noun (gsc_list_sites, resolve_entity, search_within), others are lowercased concatenations (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded), and several are bare nouns or adjectives (recent_alerts, forget, recall, process). There is no consistent verb style or separator convention across the set.

Tool Count1/5

35 tools is already heavy, but the bigger problem is that only 4 of them (gsc_list_sites, gsc_list_sitemaps, gsc_inspect_url, gsc_search_analytics) relate to the server's stated Google Search Console purpose. The remaining 31 are a sprawling Pipeworx data/prediction-market/memory toolkit, making the count wildly disproportionate to the apparent scope.

Completeness1/5

For a Google Search Console server, the surface is severely incomplete: it can list sites/sitemaps, inspect URLs, and query analytics, but lacks sitemap submission, property add/remove, URL removal/access control, and other core GSC operations. Conversely, the 31 off-domain tools make the domain itself incoherent — an agent cannot tell what this server is actually for.

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