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Glama

Spain Tenders

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

A4.9/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses concrete behaviors: data sources (SEC EDGAR/XBRL, FAERS), specific metrics pulled (revenue, income, cash, debt; adverse-event and trial counts), fiscal-year handling, and output ordering. This is rich, non-redundant 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 front-loaded with trigger phrases, then gives usage guidance, then provides type-specific details and output format. Every sentence earns its place, and the structure is easy to scan—long but purposeful.

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?

With no output schema, the description explains return values (paired data + citation URIs), sorting behavior, and data sourcing. It covers all essential aspects for an agent to invoke correctly, including the benefit of replacing many sequential calls. No significant gaps.

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% and already explains the enum and array format. The description enhances this by providing concrete examples (AAPL, MSFT; ozempic, mounjaro) and explaining how the type parameter determines which metrics are retrieved, adding meaning beyond the raw 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?

Clear verb 'compare' with explicit resource ('companies or drugs') and scope (2-5 entities). Trigger phrases like 'X vs Y' and 'rank these companies' make intent unmistakable, and it distinguishes itself from single-entity lookups 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?

States 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing explicit when-to-use guidance and a direct alternative. Also clarifies the two entity types and gives example usage for each.

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

Several clusters overlap heavily: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-identical routers (the beta currently matches stable exactly), and the Polymarket tools all circle around edge/arbitrage detection with fuzzy boundaries. The Spain tender tools and utility tools are distinct, but the overlapping clusters are enough to cause misselection.

Naming Consistency3/5

Names are uniformly snake_case and some families share clear prefixes (es_tender_*, ask_pipeworx_*, polymarket_*). However, conventions are mixed: verb-led names like compare_entities and subscribe sit alongside noun phrases like entity_profile and bet_research, so there is no consistent verb_noun pattern.

Tool Count2/5

34 tools is already in the heavy range, but the bigger problem is scope: a server named 'Spain Tenders' ships 34 tools, only 3 of which are actually Spanish-procurement tools. The rest are a broad Pipeworx/Polymarket/utility toolkit, making the count inappropriate for the declared purpose.

Completeness3/5

The three es_tender_* tools cover the core discovery workflows: keyword search, recent notices, and status filtering with budgets, deadlines, and URLs. Notable gaps remain, though: no tender detail-by-id tool, no tender-specific subscription/alerting, and no explicit region, CPV, or date-range filters.

Resources