Slander MCP

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

With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: the specific engagement formula with weighted components (likes*1.0 + retweets*2.0 + replies*0.5), the rationale for weighting ('retweets weighted highest because sharing is strong signal for humor'), and the separation logic (text vs media posts). However, it doesn't mention output format or potential limitations.

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 efficiently structured in two sentences: the first states the core functions, the second explains the engagement formula with rationale. Every element serves a purpose with zero wasted words, making it easy to parse and understand quickly.

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 tool with no annotations and no output schema, the description provides substantial context about the ranking algorithm and processing logic. It covers the core transformation behavior well but doesn't describe the output format or structure, leaving some ambiguity about what the tool returns. Given the complexity, it's mostly complete but has one notable gap.

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%, providing good parameter documentation. The description adds some value by mentioning 'posts from fetch_posts' and implying the target parameter is for nickname extraction, but doesn't significantly enhance parameter understanding beyond what the schema already provides. Baseline 3 is appropriate given high schema coverage.

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: 'rank fetched posts by engagement', 'separate text from media posts', and 'extract nicknames'. It distinguishes from sibling tools by specifying it operates on posts from fetch_posts rather than fetching or generating queries itself.

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 context by mentioning 'posts from fetch_posts' and 'required for nicknames' in the schema, but doesn't explicitly state when to use this tool versus alternatives. It suggests a workflow (use after fetch_posts) but lacks explicit guidance on when-not scenarios or comparisons with sibling tools.

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