Skip to main content
Glama

get_feedback

Retrieve AI coaching feedback for meetings, including communication scores, strengths, growth areas, and talk-to-listen ratios. Generates feedback automatically if not available.

Instructions

Get AI coaching feedback for a meeting: communication score, strengths, growth areas, and talk-to-listen ratio. Generates automatically if not yet available.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
meeting_idYesThe meeting/conversation ID
Behavior3/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It adds some context: it describes the feedback generation behavior ('Generates automatically if not yet available'), which is useful beyond basic function. However, it lacks details on permissions, rate limits, error conditions, or response format. For a tool with no annotations, this leaves significant behavioral gaps.

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 appropriately sized and front-loaded: the first sentence clearly states the purpose and outputs, and the second sentence adds critical behavioral context. Every sentence earns its place with no wasted words, making it efficient and easy to parse.

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

Completeness3/5

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

Given the tool's moderate complexity (retrieving/generating coaching feedback), no annotations, and no output schema, the description is partially complete. It covers the core function and auto-generation behavior but lacks details on output structure, error handling, or integration with sibling tools. This is adequate for basic use but leaves gaps for robust agent operation.

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?

Schema description coverage is 100%, with the meeting_id parameter fully documented in the schema. The description doesn't add any parameter-specific details beyond what the schema provides (e.g., format examples or constraints). With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract.

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 clearly states the tool's purpose: 'Get AI coaching feedback for a meeting' with specific outputs listed (communication score, strengths, growth areas, talk-to-listen ratio). It distinguishes from siblings like get_summary or get_transcript by focusing on coaching feedback rather than general summaries or raw transcripts. However, it doesn't explicitly differentiate from all possible alternatives (e.g., ask_about_meeting might also provide feedback).

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 implied usage guidance: it's for retrieving AI coaching feedback for meetings, and it mentions that feedback 'Generates automatically if not yet available,' suggesting it can be used both to retrieve existing feedback and trigger generation. However, it doesn't explicitly state when to use this tool versus alternatives like ask_about_meeting or get_summary, nor does it provide exclusions or prerequisites.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/itsconvo/mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server