get_spatial_map

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It mentions the conversion to an ASCII grid and that the output appears directly in the MCP response JSON, which is a useful behavior. Yet it does not disclose potential performance implications of processing LiDAR data, behavior on missing scan topics, latency, or any side effects. Given the tool's potentially heavy operation, this is a significant gap.

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 sentence, which is concise, but it includes the marketing phrase '[WORLD-FIRST]' that adds no functional value and could distract from the core information. The sentence is packed with detail but lacks a clear structure that separates purpose from usage context. It earns a middle score because it is not overly verbose, yet it contains superfluous hype and could be better organized.

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 has no annotations, no output schema, and two undocumented parameters, the description needs to provide substantial context. It explains the high-level purpose but does not cover parameter semantics, output format beyond 'ASCII grid', edge cases, or prerequisites (e.g., active LiDAR topic). The description leaves too many unknowns for an agent to confidently invoke the tool correctly in varied scenarios.

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 0%, and the description provides no explanation of the two parameters (grid_size and scan_topic). An agent invoking the tool would have no hints about valid values, units, or how these parameters affect the output. The description does not compensate for the schema's lack of documentation, making parameter usage entirely opaque.

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 function: converting 360° LiDAR pointclouds into a 2D ASCII spatial radar grid. It uses specific verbs and resources ('converts', 'LiDAR pointclouds', 'ASCII spatial radar grid') and mentions the output location in the MCP response, which differentiates it from simply reading raw topics. However, it does not explicitly contrast with sibling tools like read_topic or get_robot_snapshot, so it misses full sibling differentiation.

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 the tool is for gaining spatial awareness in LLMs ('allowing text & vision LLMs to see surrounding space'), providing clear context for when it might be used. However, it offers no explicit guidance on when not to use it or what alternative tools exist (e.g., read_topic for raw pointcloud data). The use-case framing is useful but lacks explicit exclusions or comparisons.

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