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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

A4.4/5.0
Behavior4/5

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

Beyond annotations (readOnlyHint, openWorldHint, idempotentHint), the description adds specific data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), fiscal-year handling, sorting by primary metric, and citation URIs, though it omits failure modes or rate limits.

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 well-structured with trigger phrases first, then purpose, then details. Every sentence adds value, though it could be slightly more concise.

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 no output schema, the description explains return format (paired data + citation URIs) and sorting. It lacks explicit mention of pagination or consistency guarantees, but is sufficient for a comparison tool.

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 coverage is 100%, but the description adds significant value: explains type with data sources (company pulls financials, drug pulls adverse events), details values format (tickers/CIKs vs drug names), and confirms min/max bounds.

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 it compares 2–5 companies or drugs side-by-side, lists trigger phrases, and distinguishes from sequential single-entity lookups by saying 'replaces 8–15 sequential lookups'.

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 when-to-use guidance ('ALWAYS PREFER over sequential single-pack lookups') and gives example queries, but does not explicitly state when not to use it (e.g., for single entities).

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

Several clusters blur together: ask_pipeworx, ask_pipeworx_beta (explicitly identical today), and ask_pipeworx_grounded share routing, while discover_tools/suggest_questions and the Polymarket edge/arbitrage/fill-risk tools have overlapping discovery purposes. Rich descriptions reduce some confusion, but an agent must read carefully to avoid misselection.

Naming Consistency3/5

Most names are lowercase snake_case, but there is no consistent verb_noun pattern: ask_pipeworx/beta/grounded and polymarket_* are domain-prefixed, entity_profile/recent_changes are noun phrases, and query/metadata/remember are bare verbs or nouns. The naming is still readable and subfamilies share prefixes, so it is mixed rather than chaotic.

Tool Count2/5

34 tools is above the comfortable range for a single MCP server, and several could be folded together (the beta variant, visibility checks, and Polymarket scanners). The breadth reflects a large platform, but the surface feels heavy for an agent to select from confidently.

Completeness4/5

The query side is strong: PA Open Data has datasets/metadata/query coverage, and Pipeworx provides ask, deep_research, entity_profile, compare, recent_changes, validate_claim, plus subscriptions and memory with lifecycle coverage. Minor gaps: descriptions promise resolvable pipeworx:// citations but no resource/read tool is exposed, and there is no direct way to fetch an arbitrary record by citation.

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