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Glama

Ebird

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?

Description adds significant behavioral details beyond annotations: data sources (SEC EDGAR/XBRL, FAERS), off-calendar fiscal year handling, result sorting by primary metric, and return format (paired data + citation URIs). No contradiction with annotations.

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 well-structured, front-loaded with trigger phrases. Every sentence adds value, though it could be slightly more concise by using bullet points for the two types.

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 lacking an output schema, the description explains return format and sorting behavior. It covers all essential aspects for a tool comparing 2-5 entities of two types, providing complete context for an agent to use 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?

With 100% schema coverage, the description adds value by explaining what each type pulls (e.g., revenue/net income for companies, adverse events for drugs) and providing example formats for values (tickers/CIKs for company, names for drug). This goes beyond the schema's brief 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 uses strong verbs like 'compare', 'rank', and provides natural language triggers. It clearly distinguishes from sibling tools like 'entity_profile' by specifying it performs side-by-side comparisons of 2-5 entities.

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 states 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', providing clear when-to-use guidance. Also mentions it replaces 8-15 sequential lookups, implying when not to use (single entity 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

A3.6/5.0
Disambiguation3/5

Several tools are close cousins: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route to the same underlying data catalog, and bet_research/polymarket_edges/polymarket_arbitrage share a betting-research niche. The eBird tools are clearly a different cluster, but the server carries so many unrelated domains that an agent may struggle to pick the right category member (e.g., stable router vs beta router vs grounded router).

Naming Consistency3/5

Almost everything is snake_case, but the pattern is not uniform: there are plenty of verb_noun names (find_species, list_subregions, scan_competitor_ai_presence) mixed with bare consumer-style names (ask_pipeworx, bet_research, entity_profile, deep_research) and short helpers (recall, forget, remember). No mixed scripting-case chaos, but no consistent verb_noun or noun_verb system either.

Tool Count2/5

36 tools is heavy for a server that calls itself Ebird: only ~5 tools actually relate to bird observation, while the rest span Pipeworx data lookups, Polymarket betting, legal/regulatory analyzers, memory, subscriptions, competency scanning, npm package checking, and llms.txt generation. The count would be reasonable for a broad data platform, but the server's stated identity and the bundled tool set do not match, making the scope feel bloated and incoherent.

Completeness2/5

For a bird-centric server, the surface is thin: you can find a species, list subregions, and pull recent/notable observations, but you cannot get coordinated eBird atlases, hotspot details, species life-history stats, or region-based species lists. For the larger set of unrelated tools, completeness is impossible to gauge about a missing domain; the eBird purpose feels unfinished even though the miscellaneous tools are overloaded.

Resources