get_statisticsGet aggregate OBIS statistics for a marine taxon: total occurrence records, distinct species/taxa, contributing datasets, and the observed year range.
| Name | Required | Description | Default |
|---|---|---|---|
| scientificname | Yes | Scientific name to summarize, e.g. "Orcinus orca". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "scientificname": "Orcinus orca"
+ },
+ {
+ "scientificname": "Gadus morhua"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, idempotent, non-destructive behavior. Description adds that it returns total occurrence records, distinct taxa, etc., but does not disclose additional behavioral traits like response size limits or data freshness.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with main purpose, no fluff. Every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given simple input (one string), rich annotations, and no expected output schema, description sufficiently explains what the tool returns. Minor gap: no mention of potential ambiguity in taxon resolution, but acceptable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single parameter 'scientificname', which already provides example. Description adds no extra meaning beyond the schema, baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description uses specific verb 'Get' and resource 'aggregate OBIS statistics for a marine taxon', listing exactly what stats are returned. Distinguishes from siblings like 'find_occurrences' (individual records) and 'get_taxon' (taxon details).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives or prerequisites. Implied by sibling names but lacks direct 'use this when' or 'instead of' statements.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tool clusters have unclear boundaries, most notably ask_pipeworx / ask_pipeworx_beta (which currently matches ask_pipeworx exactly) / ask_pipeworx_grounded, as well as bet_research and the five polymarket_* tools which all surface betting opportunities. ai_visibility_check and scan_competitor_ai_presence overlap, and entity_profile/compare_entities/resolve_entity share inputs. Extensive descriptions help differentiate, but an agent could easily misselect between near-duplicate entry points.
Names are uniformly snake_case and mostly follow verb_noun or prefix-group patterns such as ask_pipeworx_*, polymarket_*, remember/recall/forget, and subscribe/unsubscribe. Minor deviations like entity_profile and recent_changes drop the verb, but the overall convention is predictable and readable.
34 tools is beyond the 25+ threshold and the server bundles many unrelated domains—Pipeworx data research, Polymarket analysis, memory, subscriptions, AI visibility, npm scanning, and OBIS marine data. Many tools are redundant variants (six Polymarket tools, four ask_pipeworx variants) that inflate the surface area without adding distinct capabilities.
The data-query and prediction-market domains are thoroughly covered: routing, grounded answers, deep research, claim validation, entity comparison, edge detection, fill risk, and subscriptions. However, the server's namesake OBIS surface is skeletal—only occurrence samples, taxon resolution, and aggregate statistics, with no full-record access or dataset browsing—and the broad research purpose leaves notable raw-dataset access hidden behind the meta-router.