blackmount-nlp-mcp

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns the 'Length' of the LCS, which implies a numeric output and a read-only operation, but doesn't specify details like performance characteristics (e.g., time complexity for long strings), error handling (e.g., for empty strings), or output format (though an output schema exists). For a computational tool with zero annotation coverage, this is insufficient.

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 core functionality: 'Length of longest common subsequence (LCS) between two strings.' It uses no unnecessary words and immediately conveys the tool's purpose, making it highly concise and well-structured for quick understanding.

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 moderate complexity (computing LCS length), no annotations, and an output schema (which likely defines the numeric result), the description is minimally adequate. It states what the tool does but lacks details on behavior, usage context, or parameter nuances. With an output schema, it doesn't need to explain return values, but overall completeness is limited to the basic purpose.

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 description adds no parameter semantics beyond what the input schema provides. Schema description coverage is 0%, but the parameter names 's1' and 's2' are self-explanatory as strings to compare. The description doesn't clarify if they are case-sensitive, have length limits, or handle special characters. With two parameters and low schema coverage, the baseline is 3 as the description doesn't compensate for the gap but the parameters are straightforward.

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: 'Length of longest common subsequence (LCS) between two strings.' It specifies the verb ('get' implied by 'Length of') and resource (LCS between strings), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_edit_distance' or 'get_cosine_similarity' that also compare strings, which prevents a perfect score.

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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_edit_distance' for edit-based comparisons or 'get_cosine_similarity' for vector-based similarity, nor does it specify use cases (e.g., for sequence alignment vs. other string metrics). This lack of context leaves the agent to infer usage from the tool name alone.

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