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chess

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"]).

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the readOnlyHint/idempotentHint annotations, the description reveals specific data sources (SEC EDGAR/XBRL, FAERS), correct handling of off-calendar fiscal years, sorting behavior by primary metric, and return format with paired data + citation URIs. This is rich, non-obvious context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but every sentence earns its place: usage examples, preference rule, data-source details, sorting behavior, and output format. It is front-loaded with user-phrase examples that make recognition easy.

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?

For a tool with no output schema, the description fully covers input constraints, entity types, data sources, behavioral nuances, and return format. The scale note ('Replaces 8–15 sequential lookups') gives context on efficiency, making it self-sufficient.

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 covers 100% of parameter syntax, but the description adds semantic depth by explaining what each type pulls (company financials vs. drug adverse-event counts) and how values should be formatted (tickers/CIKs, drug names). This enriches the baseline 3.

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 opens with concrete example phrases and explicitly states 'side-by-side comparison of 2–5 companies or drugs in ONE parallel call', making the tool's function unmistakable. It distinguishes itself from sibling tools like entity_profile by emphasizing parallel comparison vs. single-entity 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?

The description gives an explicit directive: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities' and reinforces it by noting it 'Replaces 8–15 sequential lookups.' This clearly tells the agent when to choose this tool over alternatives.

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

B3.4/5.0
Disambiguation2/5

The server is named 'chess', yet none of the 34 tools relate to chess. An agent looking for chess functionality would find all tools irrelevant. While individual tool descriptions are clear, the server's name creates a fundamental disambiguation problem: the tool set does not match the server's apparent purpose.

Naming Consistency4/5

Tool names within the set follow a consistent snake_case pattern with descriptive verbs (e.g., ask_pipeworx, deep_research, resolve_entity). There are no mixed conventions. However, the server name 'chess' is completely inconsistent with the tool names, which all suggest data research rather than chess.

Tool Count1/5

For a server named 'chess', 34 tools is wildly excessive. Even for a data research server, the count is high, but the server's name implies a narrow chess domain, making the count inappropriate. The tools cover broad topics like SEC filings, Polymarket, and weather, none of which belong in a chess server.

Completeness1/5

The server claims to be about chess, but there are zero chess-related tools. The tool set is completely incomplete for its stated purpose. As a data research server, completeness might be high, but that is irrelevant given the server name.

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