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Compute a cap table

cap_table_compute
Read-only

Computes a cap table from a list of events (founders, priced rounds, SAFEs/notes, option pools, warrants). Returns per-round snapshots and the final ownership/dilution state. Requires a Hemrock API key with the Cap Table & Exit Waterfall product; without it you get a checkout URL. Call list_concepts/get_concept for the event structure.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
eventsYesOrdered cap-table events. Each has a "type" (common_issuance, priced_round, convertible_round, option_pool, option_grant, warrant_round, secondary_sale, manual_issuance) and a "label", plus type-specific fields. Example: [{"type":"common_issuance","label":"Founders","grants":[{"id":"f","name":"Founder","shares":8000000,"kind":"founder"}]}].

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A3.9/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and openWorldHint=true, and the description adds value by disclosing the authentication requirement (Hemrock API key) and the fallback behavior (returning a checkout URL without it). It also describes the return content (per-round snapshots and final ownership/dilution state), beyond what annotations provide.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (three sentences) and front-loaded with the core action. Each sentence contributes distinct information: computation, output, and authentication/pointer. There is no filler or redundant content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter tool with no output schema, the description adequately explains what it returns (per-round snapshots and final state) and covers critical context (API key requirement and checkout URL fallback). It does not detail the output format, but that is acceptable given the tool's moderate complexity and the provided annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema covers the single 'events' parameter with a detailed description including types and an example, so the baseline is 3. The description adds value by pointing to list_concepts/get_concept for the event structure, providing extra guidance beyond the schema. It also lists the event types, though redundant with the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses the specific verb 'Computes' and clearly identifies the resource ('a cap table'), enumerating the event types it handles (founders, priced rounds, SAFEs/notes, option pools, warrants). While it precisely defines the tool's scope, it does not explicitly distinguish it from sibling compute tools like exit_waterfall_compute or fund_economics_compute.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides contextual guidance by mentioning the required product ('Cap Table & Exit Waterfall product') and pointing to list_concepts/get_concept for the event structure. However, it does not explicitly state when to use this tool versus alternatives, nor does it exclude any specific scenarios.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4/5.0
Disambiguation4/5

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 Consistency3/5

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.

Tool Count5/5

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.

Completeness4/5

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.

Resources