request_capabilityTell this server what capability you needed but didn't find here. Requests are logged and reviewed by a human; include no secrets or personal data.
| Name | Required | Description | Default |
|---|---|---|---|
| context | No | Optional: what you were trying to do, and why the existing tools did not cover it. Up to 1KB. | |
| description | Yes | What you needed. Up to 2KB. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing behavior. It explicitly warns that requests are logged and reviewed by a human, and instructs users not to include secrets or personal data. This is meaningful context about side effects and privacy, though it doesn't describe the response/outcome after submission.
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?
Two short sentences deliver the core instruction and the critical privacy warning without any filler. The main action is front-loaded and the security caveat is integrated naturally.
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 request-logging tool with fully documented parameters, the description covers what it does, when to use it, and the human-review/privacy implications. The only omission is what the agent should expect after submitting the request, but that is a minor gap given the tool's low complexity.
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 schema already documents both parameters. The description adds no new parameter-specific details beyond the privacy guidance, which is appropriate but not compensating for a gap. Baseline 3 applies.
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 immediately states the core action: telling the server about a capability that was needed but not found. This clearly distinguishes it from the sibling tools, which perform analysis or lookup operations, whereas this is a feedback/request channel.
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 phrase 'didn't find here' provides a clear trigger condition for using the tool, and the context parameter reinforces that it is for gaps not covered by existing tools. It does not explicitly name alternatives or state when not to use it, but the condition is easy to infer.
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.
Most tools target clearly distinct functions: assessment, redaction checking, context pricing, capability requests, and search. The only potential confusion is between lookup_pattern and search_architecture, but their descriptions clearly differentiate scope — narrower pattern lookup versus full-text corpus search.
All tool names follow a consistent lowercase snake_case verb_noun pattern: assess_workspace, check_redaction, lookup_pattern, price_context_read, request_capability, search_architecture. There is no mixing of naming conventions or vague verbs.
Six tools is a well-scoped count for this server's purpose. Each tool represents a distinct capability within the agent workspace practice domain, and none feel redundant or superfluous.
The set covers the core workflows: maturity assessment, knowledge search, pattern lookup, context cost analysis, and feedback on missing capabilities. A minor gap is the lack of a dedicated tool to read or list full resources, though this is partially mitigated by the resources exposed under the architecture:// scheme.