pingHealth check — returns pong to verify MCP connectivity
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| status | Yes | Connectivity status — "ok" when the MCP server is reachable |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the specific behavioral detail that it returns 'pong' as the response format, which provides concrete behavioral context about the return value beyond the annotations' safety profile.
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 a single, efficient sentence with zero waste. Every word earns its place - it states the purpose ('Health check'), the output ('pong'), and the purpose ('verify MCP connectivity') in a highly compact format.
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?
For a zero-parameter health check tool with an output schema, the description is reasonably complete. The only minor gap is that the description doesn't detail what the output schema contains, but since an output schema exists, that burden shifts away from the description. The description adequately covers a simple tool's needs.
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 tool has zero parameters, and the schema coverage is 100% (there is nothing to describe). With 0 params, the baseline is 4 per the rubric. The description correctly doesn't fabricate parameter information that doesn't exist.
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 performs a health check returning 'pong' to verify MCP connectivity, which is a specific verb+resource. It doesn't explicitly distinguish from siblings, but the tool's purpose is inherently distinct given it's a connectivity check versus the analytical sibling tools.
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 implies usage context (verifying connectivity), which is reasonably clear for a health check tool. However, there's no explicit when-when-not guidance or mention of alternatives, though the utility of a ping in a toolset of analytical tools is largely self-evident.
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 has a distinct function: initiate_analysis starts an async job, fetch_analysis_result retrieves that job's status/result, get_market_summary returns the latest pre-computed score, and get_portfolio_insights covers multiple tickers. The only mild overlap is between fetch_analysis_result and get_market_summary, but descriptions make the difference clear.
Tool names mostly follow a verb_noun pattern (fetch, get, get, initiate), with only 'ping' breaking the convention. The mix of 'fetch' and 'get' is a minor inconsistency but doesn't obscure meaning.
Five tools is well-scoped for a stock analysis server. Each tool covers a distinct part of the workflow: initiating analysis, fetching results, getting summaries, portfolio overview, and a health check. No bloat or excessive granularity.
The analysis lifecycle is covered end-to-end: initiate, fetch, and get latest summary, plus portfolio-level insights. The only notable gap is lack of a 'list analyses' or 'cancel analysis' tool, but that's not critical for the core purpose.