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dadjokes

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. Added

TDQS

A5/5.0
Behavior5/5

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

Annotations already indicate readOnly and idempotent behavior. The description goes further by detailing exact data sources (SEC 10-K financials, FAERS counts, FDA approvals, trial counts), how off-calendar fiscal years are handled, and that results are sorted by primary metric. This adds substantial transparency beyond the annotations.

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 appropriately sized—about five sentences—but each sentence carries essential information: trigger phrases, the core function, data details, sorting behavior, and efficiency benefits. It is front-loaded with purpose and usage, making it highly scannable.

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 two-parameter tool without an output schema, the description is remarkably complete. It explains what data is pulled, how results are organized, and that returned data includes citation URIs. Edge cases like off-calendar fiscal years are noted, ensuring the agent has full context to invoke the tool correctly.

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?

Although schema coverage is 100%, the description enriches both parameters. It explains the semantic difference between type='company' and type='drug', provides concrete examples like ["AAPL","MSFT"] and ["ozempic","mounjaro"], and clarifies that values map to tickers/CIKs or drug names. This goes beyond the schema's basic type definitions.

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 identifies the tool's purpose: side-by-side comparison of 2–5 companies or drugs in a single call. It provides trigger phrases like 'X vs Y' and 'rank these companies,' and distinguishes itself from single-entity lookups by explicitly stating it should be preferred 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?

The description gives explicit usage guidance: 'ALWAYS PREFER over sequential single-pack lookups when comparing entities.' It lists concrete query patterns and explains when type='company' vs type='drug' should be used. This clearly informs the agent when to select 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

A4/5.0
Disambiguation3/5

Most tools are clearly distinct, but the three ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) are nearly identical, with the beta explicitly matching the stable version. Additionally, ai_visibility_check and scan_competitor_ai_presence overlap in purpose, creating some selection ambiguity.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern (e.g., get_joke, search_jokes, resolve_entity, validate_claim). Even the more noun-like names like entity_profile and pipeworx_feedback fit the readable convention. No mixed camelCase or erratic verb usage.

Tool Count2/5

With 34 tools, the count is far above the typical well-scoped range of 3-15. The server is named 'dadjokes' but only 3 tools actually serve that purpose; the remaining 31 are an unrelated Pipeworx research and prediction-market platform, making the scope feel bloated and misaligned.

Completeness3/5

For the nominal dad-joke domain, the surface is complete (get, random, search). However, the intended domain is ambiguous given the massive unrelated toolset; it's unclear what a user should expect the server to cover, and the analysis/prediction tools lack obvious CRUD or management counterparts.

Resources