objectFetch full details for one Smithsonian Open Access item by id — a Smithsonian content id (the "id" field from search).
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | e.g. "edanmdm-nmah_1234567". | |
| _apiKey | No | Smithsonian Open Access API key (auto-injected by the platform; or pass your own). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "id": "edanmdm-nmah_1234567"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, indicating a safe, read-only, idempotent operation. The description ('Fetch full details') is consistent but adds no new behavioral insights beyond the annotations.
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?
The description is a single sentence with no unnecessary words. It front-loads the action and the resource, making it immediately understandable. Every part contributes to clarity.
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?
For a simple fetch-by-id tool with one required parameter, rich annotations, and no output schema, the description is fully adequate. It explains what the tool does and where to get the id, meeting all contextual needs.
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 coverage is 100%, with both parameters described. The description adds value by linking the id parameter to the search result's 'id' field, providing context beyond the schema examples. The _apiKey parameter is auto-injected and not discussed, but the main parameter is well-contextualized.
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?
The description uses the specific verb 'Fetch' and identifies the resource as 'full details for one Smithsonian Open Access item'. It explicitly states the id is 'from search', distinguishing it from the sibling 'search' tool that returns lists. This clearly defines the tool's purpose.
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?
The description implies usage when a specific id is available from search results. It provides context (id field from search) but does not explicitly state when not to use it or name alternatives. The context is clear enough for an agent to decide.
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.
Many tools serve overlapping purposes, such as the three ask_pipeworx variants (stable, beta, grounded) and the six Polymarket-specific tools. While detailed descriptions help distinguish them, an agent could still confuse bet_research with polymarket_edges or the ask_pipeworx versions.
Naming is highly inconsistent: verb_noun (ask_pipeworx, compare_entities), noun_noun (bet_research, entity_profile), single verbs (forget, recall, search), and adjective_noun (recent_alerts, deep_research). No clear pattern emerges across the set.
33 tools is on the high side for a server named 'Smithsonian' that actually covers a broad range of data sources (SEC, FDA, Polymarket, npm, etc.). The number feels borderline heavy but is still manageable if the server's true purpose is general research.
The tool set covers multiple domains (company financials, drugs, economics, prediction markets, npm, museum data) with reasonable depth. Minor gaps exist, such as lack of PyPI scanning or missing update/delete operations for some memory features, but core workflows are well-supported.