get_promptsReturns task-specific Layer 2 prompts for a given template and task type. These are ready-to-use prompts for common modeling tasks.
| Name | Required | Description | Default |
|---|---|---|---|
| task_type | No | Optional task type filter. | |
| template_name | Yes | The Hemrock template being used. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / properties / template_name / enumPrevious value: -[
- "standard",
- "cap_table",
- "venture_fund",
- "runway"
-]New value: +[
+ "standard",
+ "cap_table",
+ "venture_fund",
+ "venture_fund_quarterly",
+ "runway",
+ "saas",
+ "ecommerce",
+ "unit_economics",
+ "fund_economics",
+ "fund_economics_tool_web",
+ "venture_valuation"
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true and openWorldHint=false, so the read-only nature is covered. The description adds that prompts are 'task-specific' and 'ready-to-use,' which is mildly informative, but it does not disclose any additional behavioral traits such as error conditions, edge cases, or output format. It is consistent with the annotations, so no contradiction.
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 long, front-loaded with the core action and resource, and contains no filler or redundant phrases. Every word contributes to understanding the tool's purpose.
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 two parameters and no output schema, the description provides sufficient context: it states what is returned and for what inputs. It does not need to elaborate on return values because the purpose is clear, though a bit more detail about 'Layer 2' would enrich the context slightly.
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%, so the schema fully documents both parameters. The description adds no new meaning beyond restating that the tool works with a 'template and task type,' which directly maps to the two parameters. The baseline of 3 is appropriate because the schema already carries the semantic load.
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 ('Returns') and identifies the resource ('task-specific Layer 2 prompts') alongside the input scope ('for a given template and task type'). This clearly distinguishes it from sibling tools like cap_table_compute or get_checks, which have different purposes.
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 when to use the tool ('ready-to-use prompts for common modeling tasks') and mentions the required context ('given a template and task type'), but it does not explicitly state when not to use it or name any alternative tools. It lacks the exclusionary guidance that would make it a 4.
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.