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 establish that this is read-only, idempotent, and non-destructive. The description adds useful behavioral context by disclosing result-change detection, cost metadata, and the explicit '$0.00' user charge.
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 purpose, specific output details, and cost information without any filler. The structure is front-loaded and every phrase earns its place.
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 two-parameter read-only tool with full annotation coverage and an output schema, the description conveys everything needed to select and invoke it correctly. The output schema covers return-value specifics, so no further elaboration is necessary.
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%: limit documents its max and default, and task_id documents where it comes from. The description adds no parameter-level detail beyond the single-task scope, so the schema carries the burden.
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 scope: 'Recent runs of one scheduled task' and lists the concrete outputs: returned value, change indicator, and platform cost metadata. This clearly differentiates it from siblings 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 description makes the intended use case clear: inspect the recent results of a single scheduled task. It doesn't explicitly name alternatives, but the context is strong enough that an agent can infer when to select this tool.
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.
Many tools are clearly distinct, but there are several overlapping groups: PDF extraction (extract_invoices, extract_statement, extract_tables, pdf_to_markdown), table comparison (diff_tables vs reconcile_ledger), and model pricing (list_models vs model_costs). Descriptions help clarify boundaries, but an agent could misselect without careful reading.
All names use lowercase snake_case, but the verb-noun pattern is inconsistent. Most tools are verb-first (build_app, clean_table, fetch_page), but several are noun-first (jwt_decode, regex_test, web_search), noun-only (ai_visibility, model_costs), bare verbs (recall, remember), or a full phrase (what_can_you_do). This mixed convention is still readable but not predictable.
With 34 tools, this server exceeds the 25-tool threshold for 'too many'. While the breadth covers many utility domains, the count is heavy and some tools could be consolidated or removed. A more focused set would reduce cognitive load and misselection risk.
The utility set covers web, PDF, CSV, model, task, and dev tooling well, but there are notable gaps in resource lifecycles. Apps have build/list/get but no update/delete, and memories support remember/recall but no forget. These missing operations could create dead ends for agents.