get_projectGet full details for a specific project by its slug, including before/after images and testimonial. If not found, returns a list of available slugs.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The URL slug of the project |
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| slug | No | ||
| title | No | ||
| attribution | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable behavioral context beyond annotations by specifying the not-found behavior: 'If not found, returns a list of available slugs.' This helps an agent handle an edge case without guessing.
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?
Two sentences with no filler. The core purpose and return content are front-loaded, and the fallback behavior is appended in a separate clause. Every word contributes to understanding.
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 one-parameter read-only tool with an output schema and full parameter coverage, the description is complete: it states the lookup key, the nature of the returned data, and the not-found fallback. No critical behavior is left unexplained.
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% and the single 'slug' parameter is already described as 'The URL slug of the project.' The description only repeats the concept of selecting by slug without adding new semantic detail, so it meets the baseline but does not elevate it.
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 names a specific verb ('Get'), a precise target ('full details for a specific project'), and the required identifier ('by its slug'). It also distinguishes itself from sibling tools like get_project_roi and get_project_testimonials by explicitly including before/after images and testimonial as part of the returned details.
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 clearly implies when to use it: when you need full project details for a known slug. However, it does not explicitly contrast this with sibling tools like list_projects for discovering slugs or get_project_testimonials for only testimonial data, leaving the when-not-to-use guidance to inference.
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 purposes, but get_neighborhood_project_activity and get_neighbor_permit_activity are easy to confuse, and get_material_catalog vs get_material_options have overlapping scope. The detailed descriptions mostly resolve ambiguity, so it stays above average.
Naming follows a mostly consistent get_/list_ verb-noun pattern, with a small set of other descriptive verbs like check_, estimate_, and resolve_. The neighbor/neighborhood naming collision and a few multi-word exceptions are the main inconsistencies.
At 36 tools, the surface is heavy and exceeds the 25+ threshold, spanning permits, estimates, content, reviews, financing, weather, and procurement. While each cluster has plausible value, the overall count feels bloated for an agent to navigate efficiently.
The set covers a broad lifecycle: permit research, estimates, licensing, financing, ROI, weather, project/services content, reviews, and lead submission. Minor gaps like scheduling/booking and direct estimate persistence prevent a perfect score.