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kegg-mcp-server

kegg-mcp-server

by Lucas-Servi

list_organisms

Read-onlyIdempotent

Find KEGG organism codes and names by filtering the catalog with a case-insensitive query, avoiding the need to page through thousands of entries.

Instructions

List organisms available in KEGG with their 3-4 letter codes and names.

KEGG has ~12,000 organisms, so the full list is large. Pass query to filter by organism code or name (e.g. 'Bacillus', 'hsa') instead of paging through everything.

Args: query: Case-insensitive substring filter on the organism code or name. Empty (the default) returns the first max_results organisms. max_results: Maximum number of organisms to return (capped at 100).

Input Schema

NameRequiredDescriptionDefault
queryNo
max_resultsNo

Output Schema

NameRequiredDescriptionDefault
itemsYes
totalYes
databaseYes
truncatedNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed8 schema fields changedv0.5.0
    • removedOutput schema / $defs
      Removed value: -{
      -  "ErrorResult": {
      -    "description": "Structured error returned by a tool when the KEGG API fails.\n\nA tool returns this instead of raising so the model can reason about\nthe failure and decide whether to retry, change inputs, or give up.\nA point lookup that receives a 404 uses code ``not_found``; an empty search\nremains a successful result with no items.",
      -    "properties": {
      -      "code": {
      -        "default": "kegg_api_error",
      -        "title": "Code",
      -        "type": "string"
      -      },
      -      "error": {
      -        "title": "Error",
      -        "type": "string"
      -      },
      -      "hint": {
      -        "anyOf": [
      -          {
      -            "type": "string"
      -          },
      -          {
      -            "type": "null"
      -          }
      -        ],
      -        "default": null,
      -        "title": "Hint"
      -      },
      -      "path": {
      -        "anyOf": [
      -          {
      -            "type": "string"
      -          },
      -          {
      -            "type": "null"
      -          }
      -        ],
      -        "default": null,
      -        "title": "Path"
      -      },
      -      "retryable": {
      -        "default": false,
      -        "title": "Retryable",
      -        "type": "boolean"
      -      },
      -      "status": {
      -        "anyOf": [
      -          {
      -            "type": "integer"
      -          },
      -          {
      -            "type": "null"
      -          }
      -        ],
      -        "default": null,
      -        "title": "Status"
      -      }
      -    },
      -    "required": [
      -      "error"
      -    ],
      -    "title": "ErrorResult",
      -    "type": "object"
      -  },
      -  "ListResult": {
      -    "properties": {
      -      "database": {
      -        "title": "Database",
      -        "type": "string"
      -      },
      -      "items": {
      -        "additionalProperties": {
      -          "type": "string"
      -        },
      -        "title": "Items",
      -        "type": "object"
      -      },
      -      "total": {
      -        "title": "Total",
      -        "type": "integer"
      -      },
      -      "truncated": {
      -        "default": false,
      -        "title": "Truncated",
      -        "type": "boolean"
      -      }
      -    },
      -    "required": [
      -      "database",
      -      "total",
      -      "items"
      -    ],
      -    "title": "ListResult",
      -    "type": "object"
      -  }
      -}
    • addedOutput schema / properties / database
      Added value: +{
      +  "title": "Database",
      +  "type": "string"
      +}
    • addedOutput schema / properties / items
      Added value: +{
      +  "additionalProperties": {
      +    "type": "string"
      +  },
      +  "title": "Items",
      +  "type": "object"
      +}
    • removedOutput schema / properties / result
      Removed value: -{
      -  "anyOf": [
      -    {
      -      "$ref": "#/$defs/ListResult"
      -    },
      -    {
      -      "$ref": "#/$defs/ErrorResult"
      -    }
      -  ],
      -  "title": "Result"
      -}
    • addedOutput schema / properties / total
      Added value: +{
      +  "title": "Total",
      +  "type": "integer"
      +}
    • addedOutput schema / properties / truncated
      Added value: +{
      +  "default": false,
      +  "title": "Truncated",
      +  "type": "boolean"
      +}
    • changedOutput schema / required
      Previous value: -[
      -  "result"
      -]New value: +[
      +  "database",
      +  "total",
      +  "items"
      +]
    • changedOutput schema / title
      Previous value: -"list_organismsOutput"New value: +"ListResult"
  2. Changed3 schema fields changedv0.4.0
    • addedInput schema / properties / max_results
      Added value: +{
      +  "default": 100,
      +  "title": "Max Results",
      +  "type": "integer"
      +}
    • addedInput schema / properties / query
      Added value: +{
      +  "default": "",
      +  "title": "Query",
      +  "type": "string"
      +}
    • addedOutput schema / $defs / ListResult / properties / truncated
      Added value: +{
      +  "default": false,
      +  "title": "Truncated",
      +  "type": "boolean"
      +}
  3. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly, idempotent, openWorld, and non-destructive, so safety is covered. The description adds genuinely new behavioral context: the corpus is ~12,000 organisms, an empty query silently returns only the first `max_results`, and `max_results` is capped at 100.

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?

Front-loaded with the purpose, then the filtering rationale, then per-argument detail — the reader gets the key decision information in the first two sentences. Minor redundancy between the prose hint and the Args block, but nothing wasteful.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists and annotations are rich, so the description needn't explain return shapes. The only gap is that "the first `max_results` organisms" leaves the sort/ordering of that slice unspecified, which matters when the result is truncated.

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?

Schema coverage is 0% (only bare titles), so the description carries the full burden and does: `query` is a case-insensitive substring filter on code or name with examples ('Bacillus', 'hsa') and an explicit empty-string default; `max_results` is documented with its default and an undocumented-in-schema cap of 100.

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?

States a specific verb and resource ("List organisms available in KEGG") and specifies the returned content (3-4 letter codes and names). No sibling tool in the list enumerates organisms, so the resource is unambiguous within the toolset.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives a clear usage rule: because KEGG has ~12,000 organisms, pass `query` to filter rather than paging through everything. It does not name explicit alternatives or when-not-to-use cases, but the intended workflow is clear without inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.