Server Quality Checklist

Tool Scores

• argus_statusA
  4.5/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds 'no LLM call', 'no side effects', 'Auth/limits: none beyond MCP session', and details return fields. Consistent with annotations, adds useful context beyond them.

  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?

  Description is brief, front-loaded with purpose, uses clear section breaks (When to use, Auth, Returns, Example), and every sentence adds information.

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

  Completeness5/5

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

  Given low complexity (1 optional param, no output schema, high annotation coverage), description covers purpose, usage, behavior, auth, return fields, and example. Fully sufficient for agent invocation.

  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 coverage is 100% with full description of detail parameter. Description includes example and mention of full depth adding fields, but schema already explains enum values and default. Minimal added value beyond schema.

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

  Purpose5/5

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

  States explicit verb 'Return ARGUS-3 MCP runtime status as JSON text', names specific resource 'ARGUS-3 MCP runtime', and distinguishes from siblings by contrasting with argus_ask and argus_capabilities.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines5/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  Explicitly states when to use ('before heavy argus_ask calls, or to confirm version/channel/tools') and when not to use ('to run a task' or 'list WARDEN policy detail'), naming alternatives (argus_ask, argus_capabilities).

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

• argus_capabilitiesA
  4.7/5.0
  Behavior5/5

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

  Description adds behavioral context beyond annotations: 'no LLM call', 'no provider auth required', 'Read-only, idempotent', and return format. Annotations already state readOnlyHint, idempotentHint, destructiveHint; description reinforces and expands without contradiction.

  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?

  Description is concise and well-structured: purpose, usage guidance, side effects, return format, example. Every sentence adds value, no redundancy.

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

  Completeness5/5

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

  Despite lacking output schema, description fully specifies return JSON structure. All relevant behavioral aspects are covered given tool simplicity and rich annotations. No gaps 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?

  Input schema has 100% coverage for the single boolean parameter, including description and example. Description adds an example call but no new semantics beyond schema. Baseline 3 is appropriate.

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

  Purpose5/5

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

  Description clearly lists purpose: 'List ARGUS MCP tools and WARDEN security posture as JSON text'. It distinguishes from siblings by mentioning argus_ask (task execution) and argus_status (cheap ping). Verb 'List' and resource are specific.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines5/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  Explicitly states 'When to use: discovery' and 'When NOT to use: executing a user task or a cheap ping', with alternatives named (argus_ask, argus_status). Provides clear decision criteria.

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

• argus_askA
  5/5.0
  Behavior5/5

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

  The description adds significant behavioral context beyond annotations: calls configured LLM provider, may invoke ARGUS tools, third-party MCP tools gated by WARDEN, sensitive tools deny-by-default, spend bounded by budget, not idempotent, may use network. No contradiction with annotations (readOnlyHint=false, etc.).

  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 well-organized: a summary line, usage guidelines, side effects/auth/limits, return type, and an example. Every sentence is informative without redundancy. It is concise and front-loaded with the core purpose.

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

  Completeness5/5

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

  Given the tool's complexity (agent with multiple options) and lack of output schema, the description thoroughly covers input semantics, behavioral nuances, budget limits, and return type (plain-text with potential isError). No gaps remain.

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

  Parameters5/5

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

  The input schema covers all three parameters (100% coverage). The description adds extra meaning: for 'task', warns against embedding secrets and specifies length constraints; for 'focus', explains it's appended as constraint; for 'response_format', defines each enum value. This provides clarity beyond the schema.

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

  Purpose5/5

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

  The description clearly states the tool's purpose: 'Run one bounded natural-language task through the ARGUS-3 agent and return the final answer text.' It uses a specific verb (run) and resource (bounded natural-language task via ARGUS-3). It also distinguishes from siblings by noting when to use argus_status or argus_capabilities instead.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines5/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description explicitly provides when-to-use examples (research, summarise, debug, draft) and when-not-to-use cases (interactive multi-turn chat, tasks needing human approval, pasting secrets). It also mentions alternative tools: argus_status for liveness and argus_capabilities for catalog.

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