Arclan MCP Registry

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

No annotations provided, so description carries full burden. It discloses return values (score, state, tools list, latency, test history), which is adequate for a read operation. Lacks info on authentication or side effects, but not critical for this tool.

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?

Two concise sentences: first states purpose, second covers inputs and outputs. No unnecessary words. Front-loaded and efficient.

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?

No output schema, but description adequately lists return fields (score, state, tools list, latency, test history). Parameters are covered. Error handling not mentioned, but for a simple get tool, this is acceptable.

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 100% with descriptions for both parameters. Description adds examples (e.g., 'GitHub', 'Brave Search') and clarifies that id can be a name or slug, but does not significantly extend beyond schema. Baseline of 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?

Description clearly states the verb 'Get' and resource 'full details for a specific MCP server'. It lists the return contents, distinguishing it from siblings like query_registry, recommend_server, and report_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?

Implies usage when full details of a specific server are needed, but does not explicitly state when not to use or compare with alternatives. However, the context is clear and no exclusions are needed.

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?

With no annotations, the description carries the full burden of disclosure. It explains default behavior (returns only connectable servers) and filter effects. However, it does not disclose potential error conditions, rate limits, or details about the return structure beyond the limit parameter.

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 well-structured, starting with the main action and default behavior, then listing specific filter uses, and ending with a related action. It is front-loaded and each sentence contributes useful information, though it could be slightly more concise.

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 complexity of 7 parameters and no output schema, the description covers the tool's purpose, default behavior, filter options, and even suggests a follow-up action. It lacks detail on return format but is otherwise complete for a 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?

The input schema has 100% coverage with descriptions for all 7 parameters. The description adds value beyond the schema by explaining specific use cases for parameters (e.g., 'Use production_safe=true to restrict to servers with uptime > 97% and handshake success > 95%') and clarifying defaults for the status parameter.

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: 'Search the Arclan registry for MCP servers.' It specifies the resource (Arclan registry) and the action (search), and distinguishes itself from sibling tools like get_server, recommend_server, and report_server by focusing on querying the registry with various filters.

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 provides explicit guidance on when to use the tool: 'Use this before connecting to an MCP server to check its validation status and score.' It also gives specific filter suggestions (production_safe, read_only, status=stdio) and mentions a related action after use (report_server). However, it does not explicitly state when not to use this tool compared to alternatives.

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?

No annotations are provided, so the description carries the full burden. It describes the output (scored servers with reason codes) and implies it is a read-only operation (recommendations). While it does not explicitly state side effects or authentication needs, the behavioral transparency is high for a recommendation tool.

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: the first defines the tool's purpose and output, the second provides explicit usage guidance. It is front-loaded, efficient, and contains no fluff.

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 there is no output schema and no annotations, the description covers the main purpose, output structure (ranking with reason codes), and usage context. It lacks details on default parameter behavior but the schema covers defaults. Overall fairly 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?

Schema description coverage is 100%, so the baseline is 3. The description mentions the 'task or capability query' but does not add additional meaning beyond the schema. No further parameter semantics are provided.

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 starts with a specific verb-resource pair ('Get ranked MCP server recommendations') and explicitly distinguishes itself from the sibling `query_registry` by contrasting 'opinionated ranked list' with 'filtered search'. This makes the tool's purpose very clear.

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 clearly states when to use this tool ('when you want an opinionated ranked list') and explicitly names the alternative (`query_registry`). However, it does not mention the other siblings (`get_server`, `report_server`), so it is not exhaustive but still clear for the most similar tool.

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?

No annotations are provided, so description must disclose behavior. It mentions aggregation and influence on trust scores, but lacks details on idempotency, authentication, rate limits, or response behavior. Some transparency is present but incomplete.

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?

Two focused sentences with no wasted words. First sentence states action, second gives usage context, third lists supported modes. Well-structured and front-loaded.

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 14-parameter tool with no output schema, the description is minimal. It covers basic purpose and modes but lacks details on usage frequency, error handling, duplication semantics, and integration into workflow. Adequate but could be more 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?

Schema description coverage is 100% (baseline 3). The description adds value by explaining two modes of reporting: simple outcome and structured scoring (criterion + component_score), clarifying parameter relationships beyond the schema.

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 'Submit an agent usage report for an MCP server' with specific verb and resource. It explains the purpose (influence registry trust scores) and distinguishes from sibling tools (get_server, query_registry, recommend_server) which are query-oriented.

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 explicitly says 'Call this after using an MCP server' providing clear context. However, it does not explicitly contrast with siblings or state when not to use it, limiting full guidance.

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