Stream a chunk of a pending ask() job's output.
| Name | Required | Description | Default |
|---|---|---|---|
| job_id | Yes | Job id returned by a prior ask() call. | |
| offset | No | Byte offset to start reading from. 0 for the beginning. |
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 readOnlyHint, idempotentHint, destructiveHint. Description adds context that it streams chunks of pending jobs, but doesn't detail behaviors like error handling or rate limits.
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, directly front-loaded with core purpose, no wasted words.
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 read tool with good annotations and schema, the description is nearly complete. Could mention that offset is optional, but overall sufficient.
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 coverage is 100% so both parameters are fully described in the schema. Description adds no further semantic value beyond what the schema already 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?
Description clearly states the tool streams a chunk of output from a pending ask() job, specifying the action (stream), resource (output of pending job), and distinguishing from sibling tools ask and request_status.
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?
Description implies usage after ask() to read output, but provides no explicit guidance on when to use vs alternatives, nor situations to avoid.
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 has a clear, distinct purpose: ask initiates a query job, help provides orientation, read_response streams job output, and request_status checks job progress. No overlap or ambiguity.
All tool names follow a consistent pattern: single verbs (ask, help) and verb_noun combinations (read_response, request_status), all lowercase with underscores for multi-word names.
With 4 tools covering the core workflow of asking questions and retrieving responses asynchronously, the count is well-scoped and appropriate for the server's purpose.
The tool surface fully covers the expected lifecycle: initiate a job (ask), check its status (request_status), and retrieve the result (read_response), plus help. No obvious gaps given the domain.
