get_best_practicesReturns Hemrock's financial modeling best practices and design principles for a given topic. Useful as background context for AI interactions.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | No | Optional topic filter. Returns all best practices if omitted. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, establishing that this is a safe read operation. The description adds that it returns domain-specific best practices and is meant for background context, which is useful. It does not go into deeper behavioral details like pagination or response structure, but given the annotation coverage, the additional context is sufficient to earn a midpoint score.
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, front-loaded with the tool's action, and each sentence adds value: the first defines the output, the second states its intended use. There is no redundancy or extraneous information.
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 read-only tool with one optional parameter, the description and schema together provide enough context for correct invocation. The lack of an output schema is mitigated by the clear statement of what is returned. A slight deduction for not describing the response format, but this is not a significant gap for such a lightweight tool.
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 provides 100% coverage with an optional topic parameter, an enum, and a clear description. The tool description says 'for a given topic' but does not add further syntax or behavior details beyond what the schema already offers. Thus, the description neither compensates nor detracts, warranting the baseline score.
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 the verb 'Returns' and specifies the resource as 'Hemrock's financial modeling best practices and design principles', with the scope 'for a given topic'. This clearly distinguishes it from sibling tools like get_concept or get_checks, which target different content types.
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 phrase 'Useful as background context for AI interactions' gives clear context for when to invoke. However, it doesn't explicitly state when not to use it or name alternative tools, so it stops short of full exclusionary guidance. Still, it's clear enough for typical use.
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.
Each tool has a distinct purpose: three compute tools for different financial models, two list tools for discovery, and several get_* tools for retrieving context, concepts, prompts, checks, and access info. The get_* tools are numerous but their descriptions clearly differentiate them.
Naming convention is mixed: compute tools use noun_verb (cap_table_compute, exit_waterfall_compute), while access tools use verb_noun (get_access, list_models). This is still readable and somewhat predictable, but not uniform.
11 tools is well within the typical 3-15 range and appropriate for the server's purpose of financial modeling, covering both computation and supporting documentation/discovery without excess.
The compute tools cover the core cap table, exit waterfall, and fund economics models, and the supporting tools provide extensive educational and validation resources. However, list_models suggests more model engines may exist, but only three compute tools are exposed, leaving minor gaps.