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Glama

Lichess

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
Behavior4/5

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

Annotations already declare safe, idempotent, non-destructive behavior. Description adds context: data sources (SEC EDGAR/XBRL, FAERS), off-calendar fiscal year handling, sorting by primary metric, and return format with citation URIs.

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?

Single paragraph includes all critical details without redundancy. Every sentence adds value, though slightly verbose for the purpose. Could be more structured.

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?

Despite no output schema, description fully explains return values (paired data + citation URIs), sorting behavior, and data sources, making it complete for an agent to understand usage and outcomes.

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%. Description adds meaning by explaining what data is pulled for each type and that results are sorted by primary metric, going beyond 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?

Description clearly states it compares 2–5 companies or drugs in a single parallel call, lists specific query patterns, and distinguishes from siblings by advocating preference over 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 Guidelines5/5

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

Explicitly tells when to use (comparing entities, 'X vs Y' queries) and warns against sequential single-pack lookups. Provides concrete examples and states it replaces 8–15 lookups.

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

B3.3/5.0
Disambiguation2/5

The server mixes chess tools with numerous data query tools from Pipeworx, causing significant overlap. Multiple ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and deep_research have similar purposes, making it difficult for an agent to choose correctly. Chess tools are distinct but compete with many unrelated tools.

Naming Consistency2/5

Tool names follow no consistent pattern: chess tools use mostly underscores (top_players, opening_explorer), Pipeworx tools use mixed styles (ask_pipeworx, deep_research, entity_profile), and memory/subscription tools use simple verbs (remember, subscribe). The naming is inconsistent across the set.

Tool Count2/5

With 41 tools, the count is high and unfocused. A chess server would typically have 10-15 tools; the remaining 31 tools from Pipeworx are unrelated and overwhelm the set. The server tries to cover too many domains, making it bloated for its primary purpose.

Completeness2/5

The chess-specific tools (10) cover basic queries but lack deeper chess analysis (e.g., puzzles, board evaluation). The extensive Pipeworx tools are out of scope for a Lichess server, resulting in an incomplete surface for the expected domain and an excessive surface for unrelated data lookups.

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