LexRegPulse's deep-dive analysis pieces by Lex: long-form reads on a rule, an enforcement pattern or a supervisory shift, each ending on what it changes for a bank, fintech or firm. Returns the newest posts with title, date, one-line summary, slug and canonical URL; use get_blog_post for the full text.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many posts, newest first (default 10, max 50). |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the behavioral burden. It discloses that this is a read-only listing operation, returns newest posts, and names the response fields, which is good transparency for a simple fetch. It does not cover limit/pagination behavior, but that is minor since the schema documents the limit and no auth or destructive concerns apply.
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 and mostly earns its place: the first gives useful content context, and the second states the return contract and sibling routing. The opening brand/author framing is slightly redundant with 'long-form reads,' but it is not distracting and the most actionable information is front-loaded.
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 list tool with one optional parameter, the description is complete: it explains what the posts are, what fields are returned, and how to get the full text. No output schema exists, but the description supplies the return contract directly, so an agent has everything needed to invoke it correctly.
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% coverage for the single `limit` parameter, including default and max values. The description adds no additional parameter meaning, 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 states a specific operation – returns the newest deep-dive blog posts – and enumerates the exact output fields (title, date, summary, slug, canonical URL). It also explicitly contrasts itself with get_blog_post, which provides the full text, so the agent can distinguish the list-vs-detail 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 clearly says to use get_blog_post for the full text, which indicates this tool is for browsing summaries and metadata. This explicit routing guides the agent to the correct sibling without needing to inspect schemas. Other siblings are different content types and are not likely to be confused with this one.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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