get_contextReturns the universal context-setting primer for Hemrock models, plus an optional template-specific addendum. Always run this first before any other prompts.
| Name | Required | Description | Default |
|---|---|---|---|
| template_name | No | Optional. If provided, appends a template-specific primer to the universal context. |
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?
Annotations already declare readOnlyHint=true and openWorldHint=false, so the agent knows this is a safe read. The description adds the behavioral nuance that this is a primer to run first, but does not disclose details like output formatting or side effects. Since the annotation covers the safety profile, the description adds minimal but non-essential context beyond that.
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 core function and immediately followed by the key usage instruction. Every sentence earns its place without redundancy or filler.
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-optional-parameter, read-only tool with no output schema, the description covers the main behavior, the optional parameter, and the recommended usage order. It does not elaborate on the content of the primer, but given the simplicity and the rich sibling set, the guidance is sufficient for an agent 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?
Schema coverage is 100% with template_name fully described in the input schema, so the baseline is 3. The description's phrase 'optional template-specific addendum' aligns with the schema's 'template-specific primer' but adds little new meaning; the schema already provides complete parameter semantics.
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 clearly identifies the resource ('universal context-setting primer for Hemrock models'), distinguishing it from sibling compute and getter tools. It also conveys the scope (plus optional addendum) and positions it as a precede-all tool, eliminating ambiguity about its role.
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 instruction 'Always run this first before any other prompts' explicitly states when to use the tool, establishing it as a mandatory precursor. It does not explicitly mention alternatives or exclusions, but the directive is clear enough for an agent to know this is the initial step. The optional template_name guidance is implied by 'optional template-specific addendum'.
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.