Reputation summary from the aggregate star rating and review count, benchmarked against the region average. Provide company_slug for one company, or region/category for a market overview. Review texts are not stored, so no per-review sentiment or themes are returned.
| Name | Required | Description | Default |
|---|---|---|---|
| region | No | ||
| category | No | ||
| company_slug | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It discloses a significant limitation: review texts are not stored, so no per-review sentiment or themes are returned. It also explains the benchmark against region average. This goes beyond a simple 'returns summary' and helps the agent set expectations.
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 three concise sentences, each earning its place. It front-loads the core purpose, then gives usage instructions, then a limitation. No redundant words or repetition of schema details.
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?
The tool has an output schema, so return values need not be described. The description covers purpose, usage modes, and a key limitation. However, it does not explicitly state whether company_slug and region/category are mutually exclusive or what happens if neither is provided, given all parameters are optional. This is a minor 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 coverage is 0%, so the description is essential. It explains the meaning of company_slug (targets a single company) and region/category (targets a market overview), adding semantic value beyond the bare parameter names. It does not detail parameter combinations or edge cases, but enough is provided.
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 produces a 'reputation summary' based on aggregate star rating and review count, benchmarked regionally. It lacks an explicit verb like 'returns' or 'gets', but the resource and scope are specific. The mention that per-review sentiment is not returned helps distinguish it from other analysis tools.
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?
It provides explicit guidance: use company_slug for a single company, or region/category for a market overview. It also states a key condition ('Review texts are not stored') implying when not to use this tool for sentiment analysis. It does not name alternative tools explicitly, but the context is 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.
Multiple tools have overlapping purposes: search_companies, find_best_companies, smart_match, and contractor_recommendation all find contractors; get_company, company_portfolio, and company_deep_profile all return company details; calculate_cost and project_estimator both estimate costs. This makes it difficult for an agent to reliably choose the correct tool.
Tool names mix verb-first patterns (get_*, search_*, compare_*, calculate_*, export_*, find_*, request_*) with noun-first patterns (company_deep_profile, market_report, price_comparison, etc.). All names use snake_case, but the inconsistent verb/noun placement makes the set less predictable.
24 tools falls squarely in the 16-25 'feel heavy' range. While the server covers a broad domain (companies, projects, analytics, leads, estimation), the presence of many overlapping tools suggests the count could be consolidated without losing functionality.
The toolset covers the core domain well: searching companies and projects, retrieving full details, comparing entities, estimating costs, obtaining market analytics, and managing the lead lifecycle (request_quote -> get_lead_status). Minor gaps exist (e.g., no detailed per-review text, but that is explicitly stated as unavailable), but there are no dead ends for typical workflows.