get_task_runsRecent runs of one scheduled task: what it returned, whether the result changed, and measured platform cost metadata. User charge is $0.00.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | How many recent runs, max 20, default 5. | |
| task_id | Yes | From create_task or list_tasks. |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this as read-only, idempotent, and non-destructive, so the description adds limited behavioral information. It does add that the response includes returns, change detection, and cost metadata, plus the '$0.00' user charge, which slightly enriches the annotation-covered safety profile.
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 main sentence is dense and front-loaded, covering resource and output details efficiently. The second sentence about user charge is somewhat tangential but not bloated, so overall the description remains concise.
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-only retrieval tool with full parameter descriptions, a rich output schema, and strong annotations, the description provides enough context. No critical information is missing for an agent to call this tool correctly.
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 input schema fully documents task_id and limit. The description reinforces the 'one scheduled task' scope but does not add new parameter semantics beyond the schema.
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 states a specific action — retrieving recent runs of one scheduled task — and lists concrete output facets: returned value, change status, and cost metadata. This clearly distinguishes it from task-level sibling tools like list_tasks or create_task.
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 'one scheduled task' gives clear context that the caller must identify a single task via task_id. It does not explicitly name alternatives or exclusion conditions, but the scope is clear enough for an agent to know when it applies.
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.
Every tool has a clearly distinct purpose with detailed descriptions that explicitly differentiate even close pairs like diff_tables vs reconcile_ledger and list_models vs model_costs. No two tools appear to do the same thing, and the what_can_you_do tool further resolves any confusion.
The majority of tools follow a verb_noun snake_case pattern (build_app, fetch_page, list_tasks), but several notable deviations exist: ai_visibility, china_reachability, model_costs, json_yaml, pdf_to_markdown, what_can_you_do, recall, remember, and jwt_decode. This mixed convention, while still readable, is not fully consistent.
With 34 tools, the count is high and exceeds the typical comfortable range for an MCP server. However, the server is a broad AI utility platform covering web, data, LLM, conversion, and scheduling tasks, and each tool appears to serve a distinct purpose with little redundancy, making the large but organized set borderline appropriate for its scope.
The tool surface covers a wide array of common workflows (search, fetch, table operations, PDF extraction, model comparisons, task scheduling, memory). However, check_job references deep_research, translate_pdf, and make_slides which are not present in the tool list, and there is no update tool for tasks/apps or a way to delete memories, leaving some user journeys incomplete.