Mikensey MCP Server

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

Annotations indicate readOnlyHint=true and destructiveHint=false, so the agent knows this is a safe read operation. The description adds context about the output format ('SCR-structured strategy brief with benchmarks, evidence, and specific recommendations') and data grounding ('real industry data from 53 podcast episodes'), which provides useful behavioral insights beyond the annotations. However, it doesn't mention potential limitations like response time, data freshness, or error conditions.

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 and front-loaded: the first sentence states the core purpose, followed by elaboration on the SCR framework and tool differentiation. Every sentence adds value—explaining the output format, distinguishing from siblings, and summarizing parameters and returns—with no wasted words. It efficiently balances detail with brevity.

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 complexity (generating strategic advice), annotations cover safety (read-only, non-destructive), and the schema fully describes parameters, the description provides good context: it explains the SCR framework, data source, output format, and sibling differentiation. However, without an output schema, it could benefit from more detail on the return structure (e.g., format specifics like sections or length), though it does mention 'benchmarks, evidence, and specific recommendations.'

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%, with clear descriptions for each parameter (e.g., 'Your role in the industry' for 'role'). The description adds minimal value beyond the schema: it restates 'role' and 'situation' similarly, and mentions 'topic' as 'optional focus area' without additional semantics. Since the schema already documents parameters well, the baseline score 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?

The description clearly states the tool's purpose: 'Get a McKinsey-style strategy brief structured as Situation-Complication-Resolution (SCR), grounded in real industry data from 53 podcast episodes.' It specifies the verb ('Get'), resource ('strategy brief'), format ('SCR'), and data source ('53 podcast episodes'), and distinguishes it from the sibling 'mikensey_analyze' by noting this is for 'executive-ready output' versus 'deeper analysis with specific frameworks'.

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 this tool versus alternatives: 'This is the executive-ready output tool... For deeper analysis with specific frameworks (2×2, Issue Trees, Porter's, etc.), use mikensey_analyze instead.' It clearly defines the context (generating a complete strategy memo) and names a specific alternative tool, helping the agent choose appropriately.

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