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Glama

Openrouteservice

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?

Annotations already indicate safe, read-only behavior. Description adds valuable context: for companies, it pulls specific financial data from SEC EDGAR/XBRL with proper fiscal year handling; for drugs, it pulls FAERS data. Also states results are sorted by primary metric.

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?

Two sentences, front-loaded with examples, no unnecessary words. Efficient and well-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 explains return format (paired data + citation URIs) and sorting. Covers all aspects needed for an agent to use 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?

Schema covers 100% of parameters. Description enriches by detailing the 'type' options (company vs drug) and explaining 'values' format with examples (tickers for company, drug names for drug), adding meaning beyond the schema.

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 the tool performs side-by-side comparison of 2–5 companies or drugs in one call. It specifies the verb 'compare' and the resource 'entities', distinguishing it from single-entity tools like entity_profile.

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 to prefer this tool over sequential single-pack lookups when comparing entities. Provides example queries and specifies constraints (2–5 entities, one call).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Openrouteservice toolsInspect
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TDQS

B3.2/5.0
Disambiguation1/5

The tool set is a chaotic mix of geographic routing, AI visibility, betting analysis, memory storage, and random utilities. Many tools overlap in purpose (e.g., multiple data lookup tools like ask_pipeworx, discover_tools, resolve_entity), and the domain is completely inconsistent, making it nearly impossible for an agent to distinguish which tool to use for a given task.

Naming Consistency1/5

Tool names follow no consistent pattern; they mix snake_case (ai_visibility_check, ask_pipeworx), camelCase (generate_llms_txt), and arbitrary verbs without a clear verb_noun structure. Some names are vague (processV2-like patterns are absent, but e.g., 'forget' is a single verb). This chaotic naming prevents an agent from predicting tool functions.

Tool Count1/5

With 27 tools covering routing, AI marketing, betting, memory, and more, the count is extremely mismatched for the server's implied purpose ('Openrouteservice'). Even ignoring the name, the number is high and the scope is far too broad, making the set unwieldy and unfocused.

Completeness1/5

No coherent domain can be inferred from the tool set; it is an arbitrary collection. The routing tools are present but overshadowed by unrelated tools. For any single domain (e.g., betting or routing), the surface is either incomplete or includes extraneous tools, leaving the set severely lacking a clear purpose.

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