get_conceptReturns the full text of a single Hemrock concept doc by slug. Use this to learn how a financial-modeling calculation actually works before building or auditing it. Get valid slugs from list_concepts.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | The concept slug, from list_concepts (e.g. "waterfall", "anti-dilution"). |
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 and openWorldHint=false, so the safety profile is covered. The description adds value beyond annotations by specifying that the tool returns the full text of a single doc, which implies potentially long output. It also clarifies that the slug must be from list_concepts. No contradictory statements.
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 action, and includes usage guidance without waste. Every sentence earns its place.
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 single-parameter read tool with no output schema, the description is complete: it states what is returned (full text), how to get the input (slug from list_concepts), and when to use it. The annotations cover safety, and the schema covers the parameter. No gaps identified.
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%, with the slug parameter already described as 'from list_concepts (e.g. "waterfall", "anti-dilution")'. The description repeats 'Get valid slugs from list_concepts' but adds no new semantic information beyond what the schema provides, so baseline 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 clearly states the tool returns the full text of a single Hemrock modeling concept doc by slug, identifying the specific verb, resource, and scope. It distinguishes itself from siblings like list_concepts by focusing on a single concept and from compute tools by being a read-only retrieval operation.
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 provides clear usage context: 'Use this to learn how a financial-modeling calculation actually works before building or auditing it.' It also directs users to get valid slugs from list_concepts. However, it does not explicitly mention when not to use this tool or name alternatives other than list_concepts.
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.