jobsMulti-query web research, read and synthesized into one sourced JSON brief in a single call - what would otherwise cost an agent twenty calls and its whole context window. Free status polling and result retrieval. Try GET /jobs/sample.
| Name | Required | Description | Default |
|---|---|---|---|
| subject | 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?
With no annotations provided, the description carries the full transparency burden and does a good job: it discloses the pricing model (first call free, then normal price), free status polling and result retrieval, and points to a sample endpoint. It stops short of explaining the full async job lifecycle or failure modes, but the core behavioral traits are covered.
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 short and front-loaded with the core capability, followed by cost, polling details, and a sample link. Each sentence adds distinct value; the only minor issue is the slightly run-on first sentence, but it remains efficient.
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 one parameter and no output schema or annotations, the description covers the main behavior, cost, and retrieval options. However, it leaves ambiguity about the job lifecycle (whether the call is synchronous or returns a job ID needing polling) and what exactly the 'sourced JSON brief' contains. These gaps matter for an agent deciding how to handle the response.
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 has a single 'subject' parameter with no description, and schema_description_coverage is 0%. The description does not explicitly explain that 'subject' is the research topic or what format it should take. While the name and context make it inferable, the description itself provides no direct parameter semantics beyond the schema's type and name.
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 that the tool performs multi-query web research and synthesizes results into a single sourced JSON brief, which distinguishes it from the lighter-weight siblings search and translate. It lacks an explicit action verb like 'performs' or 'creates', but the resource and outcome are unambiguous.
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 the tool is for heavy research that would otherwise require many calls ('what would otherwise cost an agent twenty calls and its whole context window'), suggesting when to prefer it over repeated searches. However, it does not explicitly state when not to use it or how to choose between jobs, search, and translate.
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.
Search and translate are clearly distinct, and jobs is differentiated by its multi-query synthesis into a JSON brief, though the name 'jobs' is vague and could initially be confused with search or background task management. The detailed descriptions resolve most ambiguity.
All tool names are lowercase single words, which is simple, but search and translate read as verbs while jobs reads as a noun. There is no verb_noun or consistent action-oriented pattern, though the naming is still readable.
Three tools is well-scoped for a research/translation utility server. Each tool covers a substantial capability and justifies its existence without redundancy.
The core workflows of web search, multi-query research, and bulk translation are covered, with job polling and result retrieval mentioned. Minor gaps exist around explicit job lifecycle management such as cancelling or listing past jobs, but agents can likely work around these.