HVTracker MCP

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds no additional behavioral context beyond 'Get' – it doesn't mention prerequisites, errors, or what happens if the name is not found. With annotations present, the bar is lower, but the description still fails to provide any incremental behavioral insight.

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, front-loaded sentence with 14 words. It conveys the essential purpose without any fluff. Every word is meaningful and earns its place.

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 tool with one parameter, clear annotations, and an output schema, the description is minimal but adequate. It doesn't elaborate on the trust profile contents or interpretation, but the output schema likely covers that. The description could be slightly more complete by mentioning what the trust profile entails.

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?

Schema description coverage is 0%, so the description must compensate. It only says 'for a tracked AI agent or framework', which loosely relates to the parameter name_or_repo but doesn't explain its format, examples, or how it should be provided. The parameter name is self-explanatory, but the description adds no deeper semantic guidance.

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 action ('Get') and the specific resource ('HVTracker supply-chain trust profile') for a tracked AI agent or framework. It implicitly distinguishes from siblings like search_agents (which likely searches agents) and verify_mcp_server (which verifies servers).

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 explicit guidance on when to use this tool vs alternatives like search_agents or verify_mcp_server. The context signals provide sibling names but the description offers no when-to-use or when-not-to-use advice. Usage is only implied by the tool's purpose.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds that results are ranked by trust score, which is beyond annotations. No contradictions.

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?

Single sentence that is front-loaded with verb and resource. Every word adds value with no waste.

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?

With output schema present, return values need no explanation. Description covers search scope, ranking, and fields. Annotations cover safety. Complete for a simple search tool.

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?

Schema coverage is 0%, so description must compensate. It clarifies that query searches by name, repo, description, and category parameter filters by category, adding meaning. However, limit parameter is not described, leaving a gap.

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?

Description clearly states it searches tracked AI agents and frameworks by multiple fields (name, repo, description, category) and notes ranking by trust score. This distinguishes it from sibling tools like check_agent_trust and verify_mcp_server.

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?

Description implies usage for searching agents by various criteria, but does not explicitly state when to choose this tool over its siblings or when not to use it. No exclusions or alternatives mentioned.

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

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

The description adds 'pre-connect trust verdict' above the annotations, which already indicate read-only, idempotent, and non-destructive behavior. The description does not contradict annotations and adds minor behavioral context, but the annotations already convey the key traits.

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, concise sentence that is front-loaded with the core action and context. Every word contributes to understanding, with no superfluous content.

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 parameter), detailed annotations, and an output schema, the description is largely complete. It states the outcome (trust verdict) and usage context, though it could benefit from noting that results may vary (openWorldHint).

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?

With 0% schema description coverage, the description compensates by stating the parameter 'server' can be an MCP server, package, GitHub repo, or agent name. However, it does not specify the expected format (e.g., name, URL), leaving ambiguity.

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 purpose: providing a trust verdict for various entities (MCP server, package, GitHub repo, agent name) before connecting an AI agent. It uses a specific verb 'verify' and distinguishes from siblings by focusing on pre-connect trust, unlike 'check_agent_trust' and 'search_agents'.

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 the tool should be used before connecting an AI agent, providing clear context. However, it does not explicitly mention when not to use it or compare with alternatives, though the context is sufficient.

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