Skip to main content
Glama
damonxue

OSSInsight MCP Server

natural_language_query

Query GitHub data using natural language to analyze repositories, developers, and organizations for open source insights.

Instructions

Query GitHub data using natural language through the OSSInsight chat interface

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural language query, e.g., 'Which repositories gained the most stars in 2023?'

Implementation Reference

  • The main execution logic for the 'natural_language_query' tool. It constructs a URL to the OSSInsight web chat interface with the provided query and returns a message directing the user there, as direct API support for natural language queries is not available.
    async function naturalLanguageQuery(query: string): Promise<any> {
      // Natural language query is likely not available via the public API
      // So we'll direct users to the web interface
      
      const webUrl = `${OSSINSIGHT_WEB_URL}/chat?question=${encodeURIComponent(query)}`;
      
      return {
        message: "Natural language queries are best handled through the OSSInsight web interface.",
        web_url: webUrl
      };
    }
  • Zod schema defining the input parameters for the natural_language_query tool: a required 'query' string.
    export const NaturalLanguageQueryParamsSchema = z.object({
      query: z.string().describe("Natural language query, e.g., 'Which repositories gained the most stars in 2023?'")
    });
  • index.ts:308-311 (registration)
    Tool registration in the ListToolsRequestHandler response, specifying name, description, and input schema converted to JSON schema.
      name: "natural_language_query",
      description: "Query GitHub data using natural language through the OSSInsight chat interface",
      inputSchema: zodToJsonSchema(NaturalLanguageQueryParamsSchema)
    }
  • Dispatch handler in CallToolRequestHandler that validates input with the schema, calls the naturalLanguageQuery function, and formats the response.
    case "natural_language_query": {
      const args = NaturalLanguageQueryParamsSchema.parse(request.params.arguments);
      const result = await naturalLanguageQuery(args.query);
      return { content: [{ type: "text", text: JSON.stringify(result, null, 2) }] };
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool queries data but doesn't describe what happens during execution—e.g., whether it's read-only, requires authentication, has rate limits, or returns structured data. For a query tool with zero annotation coverage, this leaves significant gaps in understanding its behavior and constraints.

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

Conciseness5/5

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

The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It is front-loaded with the core purpose and uses clear terminology. Every part of the sentence earns its place by specifying key details like the data source and interface.

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

Completeness2/5

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

Given the complexity of a natural language query tool with no annotations and no output schema, the description is incomplete. It doesn't explain what kind of results to expect, how queries are processed, or any limitations. For a tool that interacts with an external interface (OSSInsight chat), more context on behavior and output is needed for effective use.

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

Parameters3/5

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

Schema description coverage is 100%, with the single parameter 'query' fully documented in the schema as a natural language string. The description adds no additional parameter semantics beyond what the schema provides, such as query formatting tips or examples beyond the schema's example. Baseline 3 is appropriate since the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Query GitHub data using natural language through the OSSInsight chat interface.' It specifies the action (query), resource (GitHub data), and method (natural language via OSSInsight). However, it doesn't explicitly differentiate from sibling tools like get_developer_analysis or get_repo_analysis, which might also query GitHub data but with different approaches.

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?

The description provides no guidance on when to use this tool versus alternatives. It mentions the OSSInsight chat interface but doesn't explain when natural language queries are preferred over structured queries or how this differs from sibling tools like get_collection or list_collections. There are no explicit when/when-not statements or named alternatives.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/damonxue/mcp-ossinsight'

If you have feedback or need assistance with the MCP directory API, please join our Discord server