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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

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive. The description adds substantial behavioral context: data sources (SEC EDGAR/XBRL, FAERS/FDA), off-calendar fiscal year handling, sorting by primary metric, citation URIs, and performance benefit. This goes well 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 lengthy but each sentence delivers unique value: trigger phrases, operation, type-specific details, sorting, citations, and performance comparison. It is front-loaded and structured, though slightly verbose; a minor deduction for density.

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?

Given no output schema, the description explains return format (paired data + citation URIs), ordering, data sources, and fiscal-year correctness. It fully covers the tool's behavior for both entity types, making it self-sufficient for an agent.

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?

Schema describes type and values with examples, but description adds deep meaning: type='company' pulls specific 10-K metrics, type='drug' pulls FAERS/FDA/trial counts, and values are elaborated as 2–5 tickers/CIKs or drug names. This enriches parameter understanding beyond the 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 defines the tool as a side-by-side comparison of 2–5 companies or drugs in one parallel call, with explicit trigger phrase examples like 'X vs Y' and 'rank these companies'. It distinguishes itself from sibling tools like entity_profile by stating 'ALWAYS PREFER over sequential single-pack lookups'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly states when to use the tool: whenever comparing entities, and identifies the alternative of sequential single-pack lookups. It also clarifies the two entity types and what each returns, guiding correct selection.

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

Multiple tools are near-duplicates: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share nearly identical routing, with beta explicitly described as currently identical to stable. The six Polymarket-related tools also form a dense cluster with subtle boundaries, and discover_tools/suggest_questions overlap in onboarding purpose.

Naming Consistency4/5

Most tools follow a clear snake_case verb_noun pattern (check_password, resolve_entity, compare_entities, list_subscriptions), and family prefixes like ask_pipeworx_* and polymarket_* are applied consistently. Minor deviations exist (ai_visibility_check, pipeworx_trending), but the overall convention is predictable.

Tool Count2/5

32 tools is well past the 25+ threshold and the set feels bloated: several ask_pipeworx variants and Polymarket scanning tools could be consolidated, and unrelated utilities (check_password, scan_dependency, generate_llms_txt) are mixed into what is otherwise a data-research platform. The broad scope does not justify this many top-level entry points.

Completeness4/5

The main data-research workflow is well covered: routing, grounded verification, deep research, entity resolution/profiles, comparisons, recent changes, discovery, and feedback are all present. Memory and subscription lifecycles are also complete; minor gaps remain such as the lone password tool lacking generation or breach-checking companions, and no direct raw-fetch tool, but these are workable.

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