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Glama

COTAL actions (cotal.ai)

Request a call with the founders

cotal_request_call

Sends a discovery-call request to the COTAL founders for a company evaluating agent coordination. The team replies by email. Requires the user's work email and company.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
painNoTheir biggest coordination pain.
roleNoThe user's role.
emailYesThe user's email address. Required so the record has a traceable origin.
agentsNoHow many agents the company runs in production.
companyYesCompany name.
sandboxNoDry run: validate the call, apply rate limits, store nothing. Use it to test the integration.
frameworksNoAgent frameworks or harnesses in use (Claude Code, LangGraph, custom…).
coordinatingNoOne line: what they are coordinating.
idempotency_keyNoOpaque key (a UUID is ideal). Reuse it when retrying so the same record is returned instead of a duplicate.

Schema Changelog

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

  1. First observed

TDQS

A3.8/5.0
Behavior3/5

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

Annotations are all false and provide little information; description mentions the email reply and required fields but does not disclose side effects like record creation or rate limits (though sandbox parameter hints at it).

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?

Two sentences, no redundant information, efficient.

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 9-parameter tool with rich schema descriptions, the description is enough to understand the core action and key requirement, though it omits guidance on when to choose this over siblings.

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

Parameters3/5

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

All parameters have schema descriptions, so baseline is 3; description only reiterates the required email and company, adding little beyond the schema.

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

Purpose5/5

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

Clearly states the action (sends a discovery-call request), target (COTAL founders), and context (company evaluating agent coordination). Distinguishes from sibling tools like join_waitlist and subscribe_newsletter.

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?

Does not explicitly compare with sibling tools or state when to use this instead of others; the use case is implied by the purpose but not spelled out.

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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