Build a grounded structured dataset grid from web extraction.
Accepts a task/topic query and requested column names. Creates an isolated Postgres agent job. Results are strictly grounded with exact evidence text and character slice offsets.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| columns | No |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide no flags beyond readOnlyHint=false, openWorldHint=false, etc., so the description carries the burden. It discloses that the tool 'Creates an isolated Postgres agent job,' indicating an asynchronous or background process, and emphasizes the grounding feature with exact evidence and offsets. This adds meaningful behavioral context beyond the minimal annotations.
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 three concise sentences with no fluff. The main purpose is front-loaded in the first sentence, followed by essential details about inputs and output grounding. Every sentence 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?
Given that an output schema exists, the description does not need to explain return values. It explains the input semantics and the grounding guarantee. The mention of an 'isolated Postgres agent job' implicitly aligns with sibling tools like agent_job_status and cancel_agent_job, suggesting a way to monitor progress. The description is adequate for an agent to decide and invoke the 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 coverage is 0%, so the description must clarify parameters. It explicitly mentions the query and columns ('Accepts a task/topic query and requested column names'), which adds meaning beyond the raw schema. The limit parameter is not described, but given its default value and self-explanatory nature, the description sufficiently compensates for the majority of parameters.
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 immediately states the specific action: 'Build a grounded structured dataset grid from web extraction.' This clearly distinguishes it from sibling tools like search (simple lookup) or extract_url (single URL extraction) by specifying the deliverable (dataset grid) and the source (web extraction).
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?
It explains that the tool accepts a task/topic query and column names, which implies when to use it (to build a structured dataset with specified columns). However, it does not explicitly mention alternatives or when not to use it, but the context is clear enough for an agent to choose it for dataset construction over simple search.
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.
There is notable overlap among search, search_web, search_restaurants, and search_salons, as well as between filter_restaurants/filter_salons and search with constraints. However, descriptions clarify the intended vertical or corpus, and entity getters are distinct. The overlap is manageable but could cause misselection.
Names mostly follow a get_/list_/search_/register_/delete_/submit_/vote_ pattern in snake_case. Minor deviations like 'recall', 'remember', 'research', and 'travel_health' are less predictable but still readable. Overall consistent and clear.
38 tools is on the heavy side for a single MCP server, exceeding the typical well-scoped range. While the server covers multiple subdomains (search, travel disruptions, memory, feedback, research), the sheer number may overwhelm agents and suggests potential consolidation.
The tool surface covers core workflows: search and entity retrieval for restaurants/salons, disruption monitoring with standing queries and webhooks (register/list/delete), research submission/polling, and memory/feedback mechanisms. Minor gaps exist (e.g., no cancel for research jobs, no explicit entity list endpoint), but these are workable and do not break typical agent tasks.