cap_table_get_safe_import_consentGetSafeImportConsent reports whether the company has granted current consent for an external AI to read imported SAFE contents.
| Name | Required | Description | Default |
|---|---|---|---|
| companyId | Yes | UUID value wrapper. |
| Name | Required | Description | Default |
|---|---|---|---|
| granted | No | ||
| version | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows it is a safe read. The description adds the context that it checks 'current consent,' which is consistent but does not provide additional behavioral details beyond what annotations offer.
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 a single sentence with no wasted words, but it begins by repeating the tool name 'GetSafeImportConsent,' which is slightly redundant. Otherwise, it is efficient and front-loaded.
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 consent-check tool with one parameter and an output schema, the description sufficiently explains its purpose. It covers the core behavior and leaves return format to the output schema, making it complete enough without being verbose.
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% for the single companyId parameter, which is documented as a UUID value wrapper. The description adds no extra parameter semantics, 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 reports whether the company has granted current consent for an external AI to read imported SAFE contents. This distinguishes it from sibling tools like cap_table_grant_safe_import_consent, which actively grant consent.
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 the tool is used to check consent status before reading imported SAFE contents, providing clear context. However, it does not explicitly state when to use this tool versus alternatives like grant_safe_import_consent or import_safe.
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.
Multiple tool pairs are nearly identical (formation_extract_cap_table / formation_extract_cap_table_ocr; formation_start_formation / formation_create_formation), and several tools lack descriptions, making selection ambiguous. The scale of 202 tools with overlapping summaries (e.g., multiple cap-table summary tools) compounds the confusion.
Dominant snake_case `module_verb_noun` pattern, but with notable deviations: `captable_send_safe_for_signature` uses an inconsistent abbreviation, `check_company_name_availability` lacks a module prefix, and `get_list_` vs `list_` prefixes are mixed. Readable but not fully consistent.
202 tools is far beyond any reasonable surface for a single server, even a broad platform. The sheer number overwhelms and makes tool discovery impractical; many tools are peripheral (ads metrics) to the core formation purpose.
The domain appears well covered: formation flows, cap-table lifecycle (import, close, simulate), accounting (journal entries, periods, schedules), cards, documents, and transactions all have CRUD or lifecycle operations. Minor gaps exist (e.g., no card deletion, no counterparty creation), but they are unlikely to cause dead ends.