compute_x_signal

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

With no annotations, the description carries the full burden and it does convey that this is a pure computation producing three named outputs from mention counts. However, it does not disclose how recency share and positivity rate are derived from the optional parameters, how missing optional inputs are handled, or the return format. The core behavior is clear, but the details are thin.

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?

A single sentence that front-loads the primary output and score range, then the secondary outputs, then the data source. There is no filler, redundancy, or wasted wording; every word carries information.

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?

This tool has 5 undocumented parameters, no annotations, and no output schema, so the description must carry a heavy load. It helpfully names the three outputs since no output schema exists, but it leaves parameter-level semantics (especially founderMentions) and the exact derivation logic unexplained. An agent cannot confidently determine correct input construction or interpret the results.

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%, so the description must compensate. It provides context for mentionsCount as 'X/Twitter mention counts' and implies recent7dCount feeds recency share and that sentiment parameters feed positivity rate. However, founderMentions is never mapped to any metric, and no parameter is explicitly defined, so compensation is only partial.

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 uses a specific verb and resource: 'Compute x_signal_score (0-20) + recency share + positivity rate from X/Twitter mention counts.' It names the exact outputs, the score range, and the data source, so an agent knows precisely what the tool produces. It does not explicitly differentiate from the 18 sibling compute_* tools, but the specific output metrics make the purpose clear.

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?

There is no guidance on when to use this tool versus alternatives like compute_social_pain or compute_collection_scores, no exclusions, and no prerequisites or context for when X/Twitter mention counts are the right input. The description states only what it computes, never when it should be chosen.

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