Apex by LeadShark — discovery

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

With no annotations provided, the description carries the full burden and does well by labeling itself 'Read-only' and stating exactly what it returns. It also sets expectations by noting it has 'no LinkedIn powers', preventing misuse.

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 sentences, front-loaded with 'Read-only' and 'returns', and every clause adds value. No fluff or repetition.

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?

The description gives all essential context for a simple discovery stub: what it returns, when to call it, and what it cannot do. A minor gap is that the phrase 'real authenticated MCP server' is not expanded, but this is a minor omission for an introductory 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 tool has zero parameters, so the schema already covers everything. With 0 params, the baseline is 4, and the description adds unnecessary parameter explanation, so this score 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?

The description states a specific action ('returns what Apex by LeadShark is, tier pricing, and the URL...') and clearly identifies the resource. It also distinguishes from siblings by calling itself a 'discovery stub with no LinkedIn powers', making its scope explicit.

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?

Explicitly instructs 'Call this first', giving a clear when-to-use directive. It also warns that the endpoint is a discovery stub with no LinkedIn powers, effectively telling users when not to rely on it for LinkedIn operations.

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 explicitly declares 'Read-only', a key behavioral trait, and specifies the output is copy-paste starter prompts. With no annotations, this is sufficient disclosure for a simple zero-parameter 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 one concise sentence, front-loaded with 'Read-only' and immediately stating the function. No wasted words.

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?

The description covers what it returns, when to use it, and the intended phases (verify, discover, enrich, act). For a simple tool with no annotations or output schema, this is adequate, though it could benefit from stating how it complements siblings.

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 input schema confirms this (100% coverage). The description adds no parameter details, but none are needed; per rubric, baseline is 4.

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 'returns copy-paste starter prompts' for the first session after connecting the real Apex server, with a specific verb and resource. This distinguishes it from siblings like about_apex and get_setup_steps by its focus on post-connection session prompts.

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 gives clear context: use after connecting the real Apex server for the first session. It does not explicitly mention alternatives or exclusions, but 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.

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

With no annotations provided, the description must carry the behavioral transparency burden. It discloses the content (the 5-step flow) but does not explicitly state whether the tool is read-only, has side effects, or requires any setup. For a simple informational get tool, this is adequate but not rich.

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, compact sentence that is front-loaded with the core purpose ('5-step flow') and efficiently enumerates the steps. Every word contributes without redundancy.

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 no-parameter tool with no output schema and no annotations, the description is self-contained and fully explains what the tool returns (the exact 5-step flow). It leaves no meaningful gap in understanding the tool's purpose and content.

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 is empty, so the baseline is 4. The description adds no parameter-specific information because none is needed, and there is no ambiguity to resolve.

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 returns the 5-step flow for connecting an AI agent to the real Apex MCP server, listing the exact steps. This is specific and distinguishable from the sibling tools by content, though it does not explicitly name alternatives.

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 when one needs to set up an AI agent connection to Apex MCP, but it provides no explicit guidance on when to choose this tool over siblings like about_apex or first_plays. There is no mention of exclusions or prerequisites.

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