get_studyFetch metadata for a single MetaboLights study by its accession (e.g. "MTBLS1"): title, full description, and the organisms studied (species + tissue/part). Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | MetaboLights accession, e.g. "MTBLS1". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "id": "MTBLS1"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint and idempotentHint. Description adds behavioral detail like 'Keyless' (no authentication) and lists return fields, providing useful context beyond 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?
Single sentence with no filler, clearly states purpose, input, and output.
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 no output schema, the description adequately lists what is returned (title, description, organisms). It could be more detailed but is sufficient for the tool's simplicity.
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% and includes example and description for 'id'. Description adds little beyond the schema, so baseline 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 clearly states the verb 'fetch' and the resource 'metadata for a single MetaboLights study'. It specifies the output includes title, full description, and organisms studied, distinguishing it from sibling tools like search_studies.
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?
Description indicates usage requires a specific accession, but does not explicitly exclude other uses or mention alternatives like search_studies for broad queries.
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.
The vast majority of tools (e.g., ask_pipeworx, ask_pipeworx_grounded, bet_research, polymarket_arbitrage, etc.) are unrelated to Metabolights and overlap heavily with each other in purpose. Only two tools (get_study and search_studies) are clearly distinct and relevant to the server's stated domain.
Tool names are highly inconsistent, mixing snake_case (get_study, search_studies), camelCase (ask_pipeworx), and varied verb styles (e.g., validate_claim vs bet_research). No consistent pattern across the set.
With 28 tools, the server is extremely overpopulated for its stated purpose (Metabolights). Only 2 tools actually pertain to metabolomics study access, while the remaining 26 are general-purpose or unrelated (e.g., bet_research, polymarket_edges), making the count highly inappropriate.
The Metabolights domain severely lacks completeness: only search_studies and get_study are provided, with no create, update, delete, or upload functionality. The server fails to cover essential operations for its supposed domain.