Returns the directory of Context JSON briefing documents — structured ontological frameworks built on Universal Language primitives, designed for AI consumption. Use 'list' to see available contexts, or specify a context name to fetch it directly.
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | Which context to retrieve. 'list' returns the full directory. Other values fetch specific Context JSONs directly. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It reveals that the tool is a read-only retrieval operation ('Returns') and adds context about the document type ('designed for AI consumption'), but it does not explicitly state side effects, error behavior for invalid context names, or confirm there are no mutation risks. This is adequate for a simple getter but leaves some 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 two sentences, front-loaded with the core purpose, and each sentence earns its place. It avoids redundancy and provides just enough detail, making it concise and well-structured.
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's simplicity (one optional parameter, no output schema, no annotations), the description combined with the rich schema is complete. It explains what the tool returns, how to use the parameter, and the nature of the documents, fully equipping an agent to invoke it correctly.
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 already provides 100% coverage of the 'context' parameter, including an enum and a description explaining 'list' and specific values. The tool description largely repeats this information, adding only a general phrase about 'structured ontological frameworks.' Since the schema fully defines the parameter semantics, the description adds no significant incremental value, so baseline 3 is appropriate.
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 the tool's verb ('Returns') and resource ('directory of Context JSON briefing documents'), and even clarifies the secondary action of fetching a specific context. It distinguishes itself from sibling tools by focusing specifically on Context JSONs, which are structured ontological frameworks.
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 gives explicit instructions on how to use the tool: 'Use 'list' to see available contexts, or specify a context name to fetch it directly.' This provides actionable usage guidance for the primary parameter. It does not explicitly mention alternatives or exclusions relative to sibling tools, but the tool's specific purpose makes the intended use clear.
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.
Each tool retrieves a distinct type of information: agent card, context documents, service catalog, and research briefing. No overlap in purpose, and the descriptions clearly differentiate the content domains.
All tool names follow the exact same get_<noun> pattern, making the naming perfectly consistent and predictable.
With 4 tools, the server is tightly scoped for its informational purpose. Each tool covers a core aspect of the agency, and there are no redundant or extraneous tools.
The server covers the main informational needs about the agency, its services, context framework, and underlying language research. A minor gap is the lack of an interaction or order tool, but for an informational MCP the surface is largely complete.