get_accessReturns how to get access to a model engine: whether it is free, the price, and a checkout URL to purchase. Use when a compute tool returns a payment-required error, or to check access before running.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | Model key to check access for. |
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, so the agent knows this is a safe read operation. The description adds what information is returned (free status, price, checkout URL) but does not disclose additional behavioral traits such as side effects, external data dependencies, or limitations. Since the annotations cover the safety profile, a mid-score is appropriate.
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 only two sentences, with the first sentence front-loading the core purpose and the second providing usage guidance. Every word contributes information; there is no repetition of schema details 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?
Given the tool's simplicity (one enum parameter, no output schema, clear annotations), the description fully covers the purpose, the exact return contents, and when it should be used. Nothing essential is missing, and the openWorldHint is consistent with price information being dynamic.
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 single parameter 'model' has an enum and its own schema description, and the schema description coverage is 100%. The tool description does not add any additional meaning beyond the schema; it simply refers to 'model engine' without detailing the specific enum values. Per the baseline for high schema coverage, this is a 3.
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 specific verb (returns) and resource (how to get access to a model engine), listing the exact outputs: free status, price, and checkout URL. This distinguishes it from sibling tools like compute tools (which run models) and other get_* tools (which retrieve different information).
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 explicitly says when to use: when a compute tool returns a payment-required error, or to check access before running. It does not mention when not to use or name an alternative tool, but the context is clear and actionable, so this is stronger than merely implied usage.
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.