Poll public workspace changes using a durable numeric cursor.
| Name | Required | Description | Default |
|---|---|---|---|
| after | No | ||
| limit | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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 declare the tool as read-only, idempotent, and non-destructive. The description adds 'public workspace' scope and the durable cursor trait, which is useful behavioral context. However, it does not disclose return format, pagination behavior, or cursor invalidation semantics, so the added value over annotations is moderate.
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?
Single sentence, front-loaded with action and resource, and every word contributes. No filler or repeated schema information. Very economical.
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 only two optional integer parameters, no nested objects, and existing output schema and annotations, the description covers the core purpose and key trait (durable cursor). However, it omits practical details about how to use the parameters and when this polling tool should be chosen, so it is not fully 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 0%, and the description does not explicitly explain the 'after' or 'limit' parameters. The phrase 'durable numeric cursor' hints that 'after' is the cursor, but the description fails to clarify how the cursor is used or what 'limit' controls, leaving the agent with insufficient guidance.
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 uses a specific verb ('Poll'), names the resource ('public workspace changes'), and introduces the mechanism ('durable numeric cursor'). This clearly distinguishes it from sibling tools like get_review_queue or find_work, which serve different purposes.
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 clearly indicates this is for polling changes, not a one-time fetch, and the cursor mechanism implies repeated incremental use. However, it does not explicitly state when to prefer this over alternatives or mention any exclusions or prerequisites, so it stops short of a 5.
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.
Each tool targets a distinct resource and action: problem lifecycle, knowledge reading/search/context, candidate submission/review, and experience verification. Even similar tools like get_knowledge, search_knowledge, and retrieve_context are clearly differentiated by their descriptions.
All tool names follow a consistent snake_case verb_noun pattern with no mixed conventions or vague verbs. Names accurately reflect their actions and objects, making the set predictable and easy to navigate.
At 16 tools, the count is slightly above the typical 3-15 range but still reasonable given the multi-faceted domain (problems, knowledge, candidates, experiences). Each tool appears to have a specific purpose, though a few could potentially be consolidated.
Core workflows are covered: create/claim/manage problems, submit/review candidates, publish/retrieve knowledge, and verify experiences. Minor gaps exist such as no explicit close/cancel operation for problems, but agents can work around these with existing tools.