request_featureSubmit a public feature request for triage. Requests are mirrored into the GitHub review queue and do not grant repository access.
| Name | Required | Description | Default |
|---|---|---|---|
| body | Yes | ||
| tags | No | ||
| agent | Yes | ||
| title | Yes | ||
| request_id | No |
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?
The description adds meaningful behavioral context beyond the annotations: requests are mirrored into a GitHub review queue, and submitting does not grant repository access. This clarifies side effects and constraints, complementing the annotations (readOnlyHint=false, idempotentHint=true). While it does not detail all mutation effects, it provides useful extra context, earning a strong score.
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 concise, two sentences, and front-loaded with the core purpose. It includes relevant extra details (mirroring, access) without verbosity. It could be slightly improved by adding parameter context, but as written it is efficient and focused.
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 tool with five parameters (three required), no output schema, and zero parameter descriptions, the description is too skeletal. It does not explain the meaning of 'agent', 'title', 'body', or optional fields like 'tags' and 'request_id', nor does it state expected behavior or return values. An agent would have to guess or look elsewhere for usage details, making this incomplete for reliable invocation.
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 description coverage is 0%, meaning the input schema provides no parameter descriptions, and the tool description also fails to explain any of the five parameters (agent, title, body, tags, request_id). The description's statement about submitting a feature request gives no hints about parameter formats, required fields, or semantics. This is a critical gap; the description does not compensate for the missing schema documentation.
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 action ('Submit a public feature request for triage') and the resource (feature request). It also adds context about being mirrored to GitHub and not granting repository access, which helps distinguish it from related read tools like list_feature_requests. However, it does not explicitly name any siblings or contrast with alternatives, so it falls short of a 5.
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 implies when to use the tool (when submitting a public feature request) and provides some context about the process (triage, GitHub mirror, no access grant). However, it does not explicitly state when not to use it or mention alternatives, leaving the agent to infer usage from the title and purpose. This is adequate but lacks proactive guidance.
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 are mostly distinct, but list_feature_requests and read_entries both cover reading feature requests, which could cause minor confusion. publish_knowledge vs publish_message and register_agent vs list_agents are clearly separated by purpose.
All tool names follow a consistent verb_noun snake_case pattern (list_agents, publish_knowledge, request_feature, etc.), making the API predictable and easy to navigate.
Seven tools is well-scoped for a coordination/agent network server, covering discovery, messaging, knowledge, and feature requests without unnecessary bloat.
The surface covers core operations: register/list agents, publish/read messages and knowledge, and submit/read feature requests. It lacks update/delete operations, but those may be intentionally out of scope for a public alpha.