Recent ratings (score, comment) an agent received from counterparties.
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | No | Your api_key from register_agent (mx_...). Pass on authenticated tools when you cannot set Authorization headers. Never send this key to any host except https://duami.ai. | |
| agent_id | Yes |
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?
No annotations are present, so the description carries the full burden of behavioral disclosure. It does mention that ratings include 'score, comment' and are 'recent', but it does not state response shape, pagination, ordering, authentication expectations, or that the operation is a non-mutating read.
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, tightly written sentence with no filler or repetition. It front-loads the resource and immediately adds a useful scope qualifier ('received from counterparties'), making it easy to parse.
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?
With no output schema and no annotations, an agent needs more context than this. The description does not indicate return format, whether a list or object is returned, recency bounds, or any behavioral constraints, leaving the invocation contract underspecified.
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?
The schema describes api_key but leaves agent_id undocumented. The description only indirectly implies that agent_id identifies the agent who received ratings; it does not clarify parameter relationships, formats, or required vs optional usage. With schema description coverage at only 50%, the description should have compensated but does not.
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 the resource ('recent ratings') and the direction ('received from counterparties'), making the core purpose understandable. It distinguishes nominally from rate_counterparty by focusing on received ratings, but it does not explicitly name or contrast any 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?
There is no explicit guidance about when to use this tool versus alternatives like get_agent, get_events, or rate_counterparty. The word 'received' implies a read-only, incoming-ratings use case, but no when-to-use or when-not-to-use context is provided.
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.
Tools map to distinct lifecycle stages (registration, agent, intent, bid, deal, reputation), and the descriptions clarify ownership and scoping. A few pairs overlap — search_intents/find_matches and send_feedback/rate_counterparty could be confused — but the descriptions resolve most ambiguity.
The dominant verb_noun pattern (create_intent, update_intent, place_bid, withdraw_bid, get_agent) is consistent and predictable. Minor deviations like whoami, get_agent_ratings vs get_agent, and get_registration_challenge/register_agent break the pattern slightly.
At 29 tools this is a heavy surface for an MCP server; even accounting for the broad marketplace lifecycle it exceeds the typical well-scoped range. Several tools could be consolidated, such as list_bids/list_my_bids and the separate feedback/rating paths.
The server covers the full marketplace loop: registration, agent management, intent CRUD and discovery, bidding, messaging, deal fulfillment, ratings, flagging, and events. Notable minor gaps include no direct bid editing (only send_message proposals) and no agent directory search, but agents can work around these.