Skip to main content
Glama

Crc

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

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. The description adds valuable context: handles off-calendar fiscal years, sorts results by primary metric, returns citation URIs, and specifies the exact data pulled per type. No contradiction with 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 concise given the complexity, with front-loaded examples. Every sentence adds value, but could be slightly trimmed by removing some redundant natural language examples. Overall efficient.

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 is provided, but the description mentions paired data and citation URIs. Given the tool's complexity (two entity types with different data sources), the description covers key behavioral aspects like sorting and fiscal year handling. Could be improved by explicitly stating that results are returned in a structured format.

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?

Input schema has 100% coverage for both parameters. The description enriches meaning by explaining what each type returns (company: financial metrics, drug: adverse events etc.) and provides format examples for values (tickers/CIKs for company, drug names). This goes beyond the schema's brief 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 comparisons of 2–5 companies or drugs in one parallel call, specifies data sources (SEC EDGAR/XBRL for companies, FAERS etc. for drugs), and distinguishes from sequential lookups. It provides concrete examples of triggering phrases, making the purpose immediately clear.

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?

Explicitly advises to prefer this tool over sequential single-pack lookups when comparing entities. Provides natural language triggers and mentions it replaces 8–15 sequential lookups, giving clear when-to-use guidance. The sibling tools (entity_profile) are implicitly alternatives for single lookups.

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

All Crc toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.6/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in the prediction market domain (e.g., bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) and data querying (ask_pipeworx, ask_pipeworx_grounded, deep_research). This overlap creates confusion for an agent selecting the right tool.

Naming Consistency2/5

Naming is inconsistent: most tools use snake_case but some start with a verb (ask_, bet_, compare_) while others start with a noun (entity_profile, pipeworx_feedback, polymarket_*). There is no uniform pattern, making it harder to predict tool names.

Tool Count2/5

With 32 tools, the server is overstuffed for a single focus. It bundles checksums, AI visibility, data queries, prediction market analysis, subscriptions, memory, and more, which would be better split into separate, more focused servers.

Completeness2/5

Despite the large tool count, the server lacks completeness in key areas: no tool to place prediction market trades, no direct SEC filing detail extraction (only through generic queries), and only two checksum tools despite the server name 'Crc'. The surface feels scattershot rather than comprehensive.

Resources