nexusfeed-mcp

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

With no annotations provided, the description carries the full burden and discloses key behavioral traits: data is extracted weekly and cached, response time is <500ms, includes a '_verifiability block with extraction timestamp and confidence score', and covers specific carriers with planned updates. It does not mention rate limits, authentication needs, or error handling, but provides substantial operational context.

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 appropriately sized and front-loaded, starting with the core purpose. Each sentence adds value: data source and caching, response time, usage guidance, coverage details, and verification instructions. There is no redundant or wasted information, making it efficient and well-structured.

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 (data extraction, caching, multiple carriers) and no annotations, the description is largely complete, covering purpose, usage, behavior, and verification. With an output schema present, return values need not be explained. It lacks details on error cases or exact parameter usage, but provides sufficient context for effective tool selection and invocation.

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 0% description coverage, so the description must compensate. It implies parameters by mentioning 'carriers' and 'DOE diesel price', but does not explicitly explain the semantics of 'carriers', 'weeks', or 'include_doe_price'. The description adds some meaning (e.g., data is weekly, includes DOE price) but does not fully detail parameter usage, resulting in a baseline score.

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 with specific verbs ('Returns current LTL carrier fuel surcharge percentages and the DOE diesel price') and resources ('carrier fuel surcharge percentages'), distinguishing it from sibling tools like 'ltl_get_accessorials' or 'ltl_list_carriers' by focusing on fuel surcharge data rather than accessorials or carrier lists.

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 provides usage guidance by stating 'Use this instead of browsing carrier websites' and detailing why (JS-rendered pages, PDFs, unreliable session state). It also specifies coverage ('Covers ODFL and SAIA... Estes, ABF, R+L, TForce arriving in Sprint 4') and when to check data ('before using the data in a freight cost calculation or invoice audit'), offering clear alternatives and exclusions.

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