HVTracker MCP

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

Annotations (readOnlyHint, idempotentHint, destructiveHint) already declare it as safe. Description adds that it returns a trust profile, providing minor context beyond annotations.

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, no waste, efficient. However, it could include a brief explanation of the parameter without significant bloat.

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?

Simple tool with output schema, but missing parameter semantics is a notable gap. Adequate for overall context but not fully complete.

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 and no explanation of the single parameter 'name_or_repo' in the description, the description fails to add meaning beyond the parameter name.

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 it gets a specific resource ('HVTracker supply-chain trust profile') for a specific target ('tracked AI agent or framework'), distinguishing it from siblings like compare_agents 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?

Purpose is implied, but no explicit guidance on when to use this tool versus alternatives like compare_agents or search_agents. Missing context on prerequisites or exclusions.

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=true, idempotentHint=true, destructiveHint=false, so the description adds little behavioral context beyond mentioning an 'evidence-based verdict.' No contradictions, but minimal added value.

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 that front-loads the main action. It is concise but could benefit from structuring to explicitly list parameters.

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 low complexity (2 params, no nested objects, output schema present), the description covers the main purpose adequately. However, the lack of parameter semantics creates a gap in completeness for parameter guidance.

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 0% description coverage for its two string parameters (a, b). The description only vaguely implies they are agent identifiers but does not specify format, constraints, or examples, leaving the agent with insufficient 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 tool's purpose: comparing two AI agents side by side. It lists specific comparison aspects (trust scores, grades, runtime capabilities, verdict), distinguishing from sibling tools like check_agent_trust (single agent) or search_agents (searching).

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 is used for comparison but does not explicitly state when to use it versus alternatives. While sibling context provides differentiation, the description lacks explicit when/when-not guidance.

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.