list_databaseList all entries in a KEGG database (id + description). Useful for enumerating things like KEGG pathways ("pathway"), drugs ("drug"), or supported organisms ("organism"). Results are capped at 100 with a truncated flag. Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| database | Yes | A KEGG database name, e.g. "pathway", "organism", "drug", "compound". |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "database": "pathway"
+ },
+ {
+ "database": "drug"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as false. The description adds value by noting the result cap and truncated flag, which are behavioral traits beyond annotations. No contradiction.
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 two concise sentences with no fluff. It front-loads the core purpose and follows with usage guidance and constraints. Every sentence earns its place.
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 the tool has one simple parameter, no output schema, and clear annotations, the description adequately covers the return format (id+description), capacity limit (100), and flags. No significant gaps remain.
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 a clear description of the 'database' parameter. The description adds examples ('pathway', 'drug') and clarifies that the output includes id and description, which enhances semantics beyond the schema.
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 clearly states 'List all entries in a KEGG database (id + description)', using a specific verb and resource. It distinguishes the tool from sibling tools by specifying KEGG databases, a unique domain among the listed siblings.
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 provides examples of when to use (enumerating pathways, drugs, organisms) and mentions the 100-result cap with a truncated flag. It does not explicitly state when not to use or name alternatives, but the context is sufficient 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.
The three ask_pipeworx variants heavily overlap, with ask_pipeworx_beta currently matching ask_pipeworx exactly, and the six polymarket_* tools have blurry boundaries between scanning, arbitrage, and fill-risk analysis. The many non-KEGG research tools also make it easy to confuse unrelated purposes with the server's nominal bioinformatics focus.
All names use snake_case, but the conventions vary widely: single verbs (find, remember, subscribe), verb_noun pairs (get_entry, resolve_entity), noun phrases (entity_profile, bet_research), and vendor prefixes (pipeworx_*, polymarket_*). It is readable but not a consistent, predictable pattern.
34 tools is far beyond the well-scoped range, and the server is named 'Kegg' while only 3 of the 34 tools actually relate to KEGG bioinformatics. The rest belong to an unrelated Pipeworx data-research and prediction-market platform, an extreme mismatch between name, purpose, and tool count.
For a KEGG server, the surface is severely incomplete: only find, get_entry, and list_database exist, with no batch retrieval, cross-database queries, or pathway-organism mapping. The broader Pipeworx toolset is rich for data research but entirely disconnected from the server's stated purpose, leaving the actual domain under-covered.