feedback_reviewSubmit a review or star rating.
| Name | Required | Description | Default |
|---|---|---|---|
| tool | Yes | Tool name to review (e.g. "web-search" or "seo"). | |
| rating | Yes | Star rating 1-5 (1=poor, 3=okay, 5=excellent). | |
| review | No | Constructive feedback — what worked well and any specific suggestions to improve. |
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 indicate readOnlyHint=false, so the description's 'Submit' adds no new behavioral insight. The description does not disclose what happens after submission, e.g., whether the review is stored, can be updated, or is anonymous, even though idempotentHint=true suggests repeated submissions are safe.
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 or redundancy. It is concise, though it is so short that it sacrifices informative depth that could have been included without harming readability.
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 submit operation, the schema and annotations cover the basic requirements: required params, rating bounds, and safety hints. However, the description doesn't mention optionality of the review text, expected effects, or whether submission overwrites prior feedback, so it remains minimally adequate rather than complete.
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%, so the parameters are already well-documented in the input schema. The description adds minimal semantic value by mentioning 'review' and 'star rating', but it doesn't enrich understanding of the 'tool' parameter or any parameter interactions beyond what the schema provides.
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 a specific action ('Submit') and resource ('a review or star rating'), which is more specific than the title alone. It is not as strong as it could be because it doesn't explicitly differentiate from sibling tools like feedback_request_tool or feedback_debug, but the core purpose is unambiguous.
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 offers no guidance on when to use this tool versus alternatives such as feedback_request_tool or feedback_debug. There are no exclusions, prerequisites, or conditions given, leaving the agent to infer the appropriate context from the tool's name alone.
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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Tools are grouped by clear resource prefixes (account_, brain_, connector_, credential_, file_, job_, key_), and most actions have distinct purposes. A few boundaries overlap—brain_admin's lint action duplicates brain_lint, and account_preferences/setup/switch could momentarily confuse—but the descriptions resolve most ambiguity.
The dominant pattern is resource_verb for actions (file_read, job_cancel, key_create) and resource_noun for state views (credits_balance, brain_settings, account_preferences), which is readable. However, exceptions like discover, use_tool, top_up_credits, and feedback_request_tool break the pattern, and the set is not consistently verb_noun.
47 tools is well beyond the comfortable range; even though prefixes organize them, the agent faces a large selection surface with many narrowly scoped tools. A more consolidated set with action-based subcommands would be easier to navigate.
Core workflows are covered end-to-end: account setup and billing, connector and credential management, file CRUD, job polling, key lifecycle, brain knowledge management, and catalogue discovery/execution. Gaps are minor—outfit/persona/product/scene are list-only, connectors lack an update operation, and there is no explicit single-page brain get—but agents can generally work around them.