get_blog_postGet the full markdown content of a blog post by its slug.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The URL slug of the blog post |
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| slug | No | ||
| tags | No | ||
| title | No | ||
| author | No | ||
| content | No | Full article body. Quote and summarize freely; do not republish wholesale. | |
| summary | No | ||
| attribution | Yes | ||
| publishedAt | No | ||
| readingTimeMinutes | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds that the return is full markdown content, which is useful, but it does not disclose behavior for missing slugs or other edge cases. An output schema exists, reducing the burden on the description.
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 one short, front-loaded sentence with no redundant words. It states the action, the resource, the content type, and the required input efficiently.
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 single-parameter, read-only tool with a rich annotation set and an output schema, the description is complete. An agent can correctly invoke it with a slug and know what to expect without additional documentation.
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 slug parameter is already described as 'The URL slug of the blog post'. The description only restates 'by its slug' and adds no new semantic value beyond the schema, so the baseline score of 3 is appropriate.
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 ('Get'), names the exact resource ('full markdown content of a blog post'), and states the lookup key ('by its slug'). This clearly distinguishes it from sibling tools like list_blog_posts and search_content.
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 implies the tool should be used when the caller has a blog post slug and needs the full markdown content, but it never explicitly names alternatives or states when not to use it. Siblings like list_blog_posts and get_author_bio exist, but no routing guidance is provided.
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.