Compare two to six businesses after the user has gathered website and review scores. Calculates a transparent shortlist score using 60% review trust and 40% website evaluability.
| Name | Required | Description | Default |
|---|---|---|---|
| candidates | Yes | Two to six businesses using comparable evidence. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It discloses the scoring methodology and the valid input size (two to six), which goes beyond the schema. It does not describe the output format or side effects, but a pure comparison tool implies no side effects.
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 two sentences with no fluff: the first states the scope and prerequisite, the second states the calculation formula. Every clause adds value.
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 simple one-parameter tool with a well-described nested schema, the description covers purpose, prerequisites, and scoring formula. It omits an explicit description of the return type, but the calculation nature and sibling context make the output inferable.
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 schema already provides 100% parameter coverage with descriptions for each field. The description adds the critical semantic meaning: the 60/40 weighting between review trust and website evaluability, which is not present in the schema.
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 names the action ('Compare two to six businesses') and specifies the exact scoring formula (60% review trust, 40% website evaluability), distinguishing it from siblings like inspect_website or score_website_evaluability.
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 states the prerequisite condition ('after the user has gathered website and review scores'), providing clear timing. It does not explicitly mention alternatives or when not to use the tool, but the workflow context makes the intended usage clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have clearly distinct roles: inspect_website gathers facts, score_website_evaluability produces a rubric score, organize_review_evidence handles review signals, compare_shortlist_candidates merges scores, and generate_paid_scope produces a brief. The only mild ambiguity is between inspect_website and score_website_evaluability, since both involve website inspection, though their outputs differ.
All tool names follow a consistent verb_noun pattern: compare, generate, inspect, organize, and score are clear actions with descriptive objects. The naming is predictable and makes the tool set easy to navigate.
Five tools is well-scoped for the server's niche buyer-readiness purpose. Each tool contributes to a focused workflow without redundancy or unnecessary sprawl.
The workflow covers website inspection, website scoring, review evidence organization, comparison, and downstream brief generation. However, compare_shortlist_candidates expects a review trust score, and no tool actually computes that score from organized review evidence, leaving a notable gap in the core shortlisting flow.