cap_table.get_usageRead the selected project's cap-table and stakeholder limits; optionally inspect one cap table's usage.
| Name | Required | Description | Default |
|---|---|---|---|
| project_id | Yes | ||
| cap_table_id | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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, idempotentHint=true, and destructiveHint=false, and the description is consistent with those. The description adds some context by indicating project-level behavior and optional cap-table-level inspection, but it does not clarify how usage/limits are aggregated or what exactly is included.
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, front-loaded sentence with no filler. The semicolon cleanly separates the main behavior from the optional specialization, making it easy to scan.
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 output schema, rich annotations, and low parameter complexity, the description is nearly sufficient. The only notable gap is a slight ambiguity between 'limits' and 'usage', but this does not block correct tool selection or invocation.
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 0%, so the description must compensate. It loosely maps to the parameters by referencing 'selected project' for project_id and 'optionally inspect one cap table' for cap_table_id, but it provides no additional detail on constraints, null behavior, or the relationship between the two parameters.
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 clear verb ('Read') and identifies the resource: the project's cap-table and stakeholder limits, with optional per-cap-table usage inspection. It is distinguishable from cap_table.get because it focuses on usage/limits, though it does not explicitly contrast with sibling tools.
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 this tool should be used when the agent needs usage or limit information, and it notes the optional cap-table scope. However, it does not provide explicit when-to-use/when-not-to-use guidance or name alternatives among the many cap_table.* siblings.
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.
Domain prefixes (crm., cap_table., landing.) clearly separate broad modules, and most tools target a specific resource and action. However, within modules there are boundary overlaps—crm.add_contact_note vs crm.log_activity and cap_table.dilution_preview vs cap_table.simulate_raise—where descriptions must be read carefully to avoid a wrong pick.
The dominant pattern is module.verb_noun (e.g., crm.create_lead, cap_table.update_stakeholder), which is clear and readable. But a subset of top-level tools uses object_verb with flat underscores (e.g., shortlink_create, qr_generate, campaign_archive) and one outlier (campaign_stats) breaks the verb pattern, so conventions are mixed.
86 tools is an extreme surface for any single MCP server, well past the 50+ threshold that makes coherent selection impractical. Even though the features span several business domains, this would be far more usable split into focused servers per module.
The covered domains are broadly complete: cap table, CRM, incorporation, landing, projects, sparkroom, tasks, and validation all have read/write workflows with few dead ends. Minor gaps remain (no campaign listing/update, no branding palette delete/update, no contact deletion) but none of them blocks the main product workflows.