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Glama

Jsonschema

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

A4.8/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive, but the description adds significant context: data sources (SEC EDGAR/XBRL for companies, FAERS for drugs), fiscal year handling, metric sorting, and return format with pipeworx:// citation URIs. This goes well beyond the annotation baseline.

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 dense but every sentence contributes: trigger phrases, preference rule, data-source details, sorting behavior, and return format. It is slightly longer than ideal but well-organized and front-loaded with usage cues. No fluff, though a few phrases could be tightened.

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 2 simple parameters and no output schema, the description covers all critical aspects: input types, data sources, sorting, output format (paired data + citation URIs), and efficiency rationale. Combined with strong annotations, the agent has everything needed to invoke correctly.

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 both type and values described, but the description adds meaningful enrichment: explains what each enum value retrieves (10-K metrics vs. adverse-event counts) and provides input examples ("AAPL", "MSFT", "ozempic"). This adds value beyond the schema, though the schema already covers the essentials.

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 as a side-by-side comparator for 2–5 companies or drugs, with specific trigger phrases like "X vs Y" and "rank these companies." It distinguishes itself from siblings by emphasizing it replaces sequential lookups, making its purpose unmistakable.

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?

Provides explicit when-to-use guidance with example queries and domain (company/drug) specifics. It directly states "ALWAYS PREFER over sequential single-pack lookups," naming the alternative behavior to avoid, and quantifies efficiency savings ("Replaces 8–15 sequential 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

A3.8/5.0
Disambiguation2/5

ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share the same routing and response shape, while the polymarket_* family and company-research tools (entity_profile, compare_entities, recent_changes) have overlapping triggers. The descriptions are detailed, but an agent can still easily select the wrong variant.

Naming Consistency3/5

Names are readable and consistently snake_case, with recognizable families like ask_pipeworx_*, polymarket_*, and pipeworx_*. However, conventions mix verb_noun patterns (compare_entities, resolve_entity) with noun phrases (entity_profile, recent_alerts, pipeworx_trending), so the pattern is not fully consistent.

Tool Count2/5

32 tools is heavy for any single server, and nearly all of them are unrelated to the 'Jsonschema' name—only validate_json_schema actually addresses JSON Schema. Even viewed as a Pipeworx data/research toolkit, the set feels bloated with near-duplicate query and prediction-market variants.

Completeness2/5

If the intended domain is JSON Schema, the surface is severely incomplete: only validation exists, with no parsing, generation, or schema-management tools. If the intended domain is the Pipeworx data-research suite, it is more complete but still lacks a raw record-fetch tool despite citations promising pipeworx:// URIs, and the server-name mismatch creates a confusing dead end.

Resources