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TogoMCP

TogoMCP

by dbcls
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Python
Hybrid

get_sparql_endpoints

Read-only

Retrieve available SPARQL endpoints and their associated databases from the RDF Portal. Returns mappings of endpoints to URLs and database details for querying biological data.

Instructions

Get the available SPARQL endpoints for RDF Portal.

RETURNS a dict with two keys: databases (maps each database -> {url, endpoint_name, keyword_search}) and endpoints (maps each endpoint_name -> {url, databases}).

Returns: Dict with two keys: - databases: Dict mapping database -> {url, endpoint_name, keyword_search} - endpoints: Dict mapping endpoint_name -> {url, databases}

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_sparql_endpoints"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already communicate that this is a read-only, open-world operation, which covers the main safety profile. The description adds the RDF Portal scope and return-key vocabulary but does not disclose additional behavioral details like authorization requirements, rate limits, or failure modes; this is adequate but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The opening sentence is concise and front-loaded, but the description repeats the same return information twice: once in prose ('RETURNS a dict with two keys...') and once in a 'Returns:' block. This redundancy prevents every sentence from earning its place.

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

Completeness5/5

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

For a zero-parameter, read-only discovery tool with readOnlyHint and openWorldHint annotations plus an output schema, the description provides sufficient context. It covers the purpose, the resource scope, and the expected return structure, leaving no material gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has zero parameters and the schema coverage is complete, so there is no parameter ambiguity for the description to resolve. Per the baseline rule for tools with no parameters, a score of 4 is appropriate.

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?

The description uses a specific verb ('Get') and a specific resource ('available SPARQL endpoints for RDF Portal'), making the tool's function immediately clear. It also distinguishes this tool from sibling tools like 'run_sparql' by focusing on endpoint discovery rather than query execution.

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

Usage Guidelines2/5

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

No when-to-use guidance or alternative-recommendation is provided. The description only restates the action and return structure, so the agent must infer that this tool is meant for discovering endpoints before running SPARQL queries.

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

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