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Serper

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"]).

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 specific data sources (SEC EDGAR/XBRL, FAERS), handling of off-calendar fiscal years, sorting by primary metric, and return format (paired data with citation URIs). These details add value beyond annotations, which already indicate read-only, idempotent, and non-destructive behavior.

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 front-loaded with example queries and uses efficient language. It is dense but not overly verbose; however, it could be slightly more structured by separating usage examples from data source details. Still well within acceptable conciseness.

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 when to use the tool, what it does, data sources for each type, sorting behavior, output format, and parameter details. Given the lack of an output schema, the description adequately explains return values. No critical gaps identified.

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%, so baseline is 3. The description adds significant meaning by explaining what each enum value pulls ('company' fetches financial data, 'drug' fetches adverse events) and specifying format for the 'values' parameter (tickers/CIKs for companies, drug names). This exceeds the schema 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 performs side-by-side comparison of 2-5 companies or drugs in one parallel call. It includes example natural language triggers and distinguishes itself from sequential lookups, making the purpose very specific and unambiguous.

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 preferring this tool over sequential single-pack lookups for comparisons, providing strong positive guidance. However, it lacks explicit exclusions or alternatives for non-comparison scenarios, which would make it a 5.

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
Disambiguation3/5

The ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) has blurry boundaries — ask_pipeworx_beta is explicitly identical to ask_pipeworx today — and the six Polymarket tools (polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, etc.) all operate in the opportunity-detection space. Extremely detailed descriptions help, but an agent could easily select the wrong variant.

Naming Consistency3/5

Snake_case is used throughout and the polymarket_* and pipeworx_* clusters are internally consistent, but the set mixes verb-first names (ask_pipeworx, resolve_entity, validate_claim) with noun-first names (entity_profile, recent_changes, news, places) roughly evenly. The 'beta' suffix on a stable production tool and the adjective-noun 'deep_research' add further inconsistency.

Tool Count2/5

At 34 tools this exceeds the 25+ threshold, and the count is padded with redundancy: three near-identical ask_pipeworx variants, ai_visibility_check wrapped by scan_competitor_ai_presence, and six overlapping Polymarket tools. The unusually broad multi-domain scope justifies more tools than a typical server, but several clusters could be consolidated.

Completeness4/5

The surface is thorough for a read-only data-access gateway: universal routing, grounded verification, entity resolution/profiles/comparisons, web/news/maps search, prediction-market analysis, memory CRUD, and a full subscription lifecycle. Minor gaps exist (no direct fetch tool for pipeworx:// citation URIs, no image/video Serper endpoints) but agents can work around them.

Resources