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Glama

Jina Reader

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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnly, openWorld, idempotent, non-destructive. Description adds off-calendar handling, sorting by metric, citation URIs, and replaces multiple lookups. No contradictions.

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?

Front-loaded with trigger queries and preference rule. Each sentence adds value. Slightly verbose but well-structured and informative.

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?

No output schema but description explains return format (paired data + citation URIs). Covers main use cases and behavior adequately.

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% with descriptions. Description adds detailed behavior per enum value and example formats for values, exceeding baseline.

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?

Description clearly states side-by-side comparison of 2-5 companies/drugs with specific trigger phrases. Distinguishes from sequential lookups and specifies data sources per type.

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?

Explicit preference over sequential lookups is stated. Example queries guide when to use. Missing explicit exclusions but context with sibling tools provides clarity.

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

All Jina Reader toolsInspect
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TDQS

A3.8/5.0
Disambiguation3/5

Most tools have clearly distinct roles, but there is notable overlap in the ask_pipeworx family (stable, beta, grounded, deep_research), and ask_pipeworx_beta is explicitly described as currently identical to ask_pipeworx. discover_tools and suggest_questions also both serve as meta-guidance, creating some selection ambiguity. The very detailed descriptions help, but they do not fully remove the risk of misselection.

Naming Consistency3/5

The set mixes verb-first names like compare_entities and validate_claim with noun-first names like entity_profile and polymarket_edges, along with prefix families (polymarket_*, pipeworx_*, ask_pipeworx_*) and bare verbs like forget and search. Everything is snake_case and readable, but there is no single predictable convention across the whole surface.

Tool Count2/5

33 tools for a server labeled 'Jina Reader' is a sprawling surface spanning data routing, prediction markets, memory, subscriptions, and web reading. Even if each tool is individually focused, the count and combined scope feel oversized relative to the server name and the rubric's guidance for well-scoped servers.

Completeness4/5

Each sub-domain has good lifecycle coverage: memory (remember/recall/forget), subscriptions (subscribe/unsubscribe/list_subscriptions/recent_alerts), entity research (resolve_entity/entity_profile/compare_entities/recent_changes/validate_claim), and Polymarket analysis (edges/arbitrage/fill_risk/kalshi_spread). There are minor gaps like no order execution or simple page summarization, but those appear intentionally out of scope, leaving the surface largely complete.

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