slc_sparql
Run SPARQL queries against the Shanghai Library open data graph to retrieve structured information from its linked data datasets.
Instructions
SPARQL 图查询说明(该平台 Key 仅网页端可用)
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Run SPARQL queries against the Shanghai Library open data graph to retrieve structured information from its linked data datasets.
SPARQL 图查询说明(该平台 Key 仅网页端可用)
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits, but it only mentions the platform key availability for web use. It fails to indicate whether the tool performs read-only queries, any side effects, return formats, or error behavior, leaving significant gaps.
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 extremely short, but it is under-specified rather than appropriately concise. The single sentence does not earn its place because it fails to communicate the tool's function, making it more a fragment than a useful explanation.
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?
The tool has no output schema, no annotations, and a bare-bones description. For a complex operation like SPARQL graph querying, this is severely incomplete. It lacks critical information about return values, usage conditions, and alternates, making it inadequate for an AI agent.
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?
The input schema has zero parameters, so the baseline for this dimension is 4. There are no parameter definitions to clarify, and the description does not need to compensate for missing parameter info. It adds no parameter semantics, but none are required.
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 'SPARQL 图查询说明(该平台 Key 仅网页端可用)' is vague; it reads as an explanatory note rather than a clear action like 'execute SPARQL query'. It does not differentiate from sibling tools such as slc_api or slc_endpoints, leaving the tool's exact function ambiguous.
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?
No guidance is provided on when to use this tool versus alternatives. The only context is a platform key limitation, which addresses authentication but not usage scenarios, prerequisites, or complementary tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/FreyaBit/OpenSH-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server