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Glama

Rcsb Pdb

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

A5/5.0
Behavior5/5

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

The description adds rich behavioral context beyond annotations: it explains off-calendar fiscal year handling, sorting by primary metric, return format (paired data + pipeworx:// URIs), and performance (replaces 8–15 sequential lookups). No contradiction with readOnly/idempotent 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 dense but well-structured: front-loaded with natural language triggers, followed by the core purpose, type-specific details, sorting behavior, and return format. Every sentence contributes unique value with no fluff or repetition.

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, the description covers what data is returned (paired data + citation URIs), sorting behavior, entity types, fiscal year handling, and when to use. It is complete for a tool of this complexity, addressing both input semantics and output expectations.

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?

Schema has 100% coverage, but the description enriches both parameters: 'type' is explained with enum semantics and data sources, and 'values' gets examples (tickers/CIKs, drug names) and constraints (2–5 items). This goes well beyond the schema's short 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 a single parallel call, using specific examples like 'Compare X and Y' and 'which is bigger'. It distinguishes from sequential single-pack lookups, making the purpose unambiguous.

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 instructs 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing clear when-to-use guidance. It also details what data is pulled for each type (company vs drug), enabling appropriate selection based on the user's query.

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

A3.7/5.0
Disambiguation2/5

ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are nearly identical in purpose, and structure/summary both fetch PDB entries. The server name 'Rcsb Pdb' doesn't match most tools, which are Pipeworx data tools, compounding ambiguity.

Naming Consistency3/5

Mostly snake_case verb_noun, but verbs are inconsistent (ask, discover, generate, list, recall) and some names are noun phrases (entity_profile, polymarket_edges). No clear pattern unifies the set.

Tool Count2/5

37 tools is excessive for a server ostensibly about RCSB PDB; only 6 tools relate to PDB while 31 serve unrelated Pipeworx functionality. The count feels like a bundled grab-bag rather than a focused toolset.

Completeness3/5

The PDB-specific tools cover the core operations (search, fetch, assembly, ligand, polymer entity), so the structural biology surface is mostly complete. However, the server's overall purpose is muddled, and the Pipeworx tools are a separate domain that happens to be bundled in, making it unclear what 'completeness' even means for this server.

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