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farukkolip

xtapdown-mcp

calculate_x_engagement_rate

Calculate X (Twitter) engagement rate from likes, reposts, replies, bookmarks, and quotes relative to followers. Returns percentage and benchmark band (Excellent to Inactive).

Instructions

Calculate X (Twitter) engagement rate using the public follower-based formula: (likes + reposts + replies + bookmarks + quotes) / followers × 100. Returns the percentage and the tier-aware benchmark band (Excellent / Good / Average / Low / Inactive).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
likesYesLikes on the post (or sum across posts)
postsNoNumber of posts the engagement is summed over (default 1)
quotesNoQuote tweets (optional, default 0)
repliesYesReply count
repostsYesReposts / retweets
bookmarksNoBookmarks (optional, default 0)
followersYesAccount follower count
Behavior3/5

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

No annotations provided. Description discloses the formula and output format (percentage and benchmark band). It does not mention error handling, data sourcing, or restrictions beyond the schema. Adequate for a calculation tool but could elaborate on behavior (e.g., it uses provided numbers, not live data).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence that efficiently conveys the formula, output, and benchmark. No superfluous text; every part earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given full schema coverage and no output schema, the description sufficiently explains the tool's purpose and return value. It could mention that followers must be positive (implied by schema exclusiveMinimum:0) but overall complete for agent usage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, with each parameter described. The description adds the formula context but does not enhance parameter meaning beyond the schema. Baseline 3 as schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool calculates X engagement rate using a specific formula and returns percentage with benchmark band. Verb 'calculate' and resource 'X engagement rate' are explicit. Distinguishes from siblings like 'build_x_search_url' or 'calculate_x_ads_revenue'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Description indicates when to use the tool: when needing to compute engagement rate via the follower-based formula. It does not explicitly state when not to use it or list alternatives, but the context is sufficiently clear given sibling tools.

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

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