Extract clean text content from any URL. Use this when a user shares a link (competitor site, news article, client brief, trend report) and wants to cross-reference it against Fodda knowledge graphs. Returns structured text ready for analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL to read and extract content from | |
| userId | No | Optional user identifier for usage tracking. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only and idempotent; the description adds valuable behavioral specifics, namely that the tool extracts 'clean text' and returns 'structured text ready for analysis.' It does not disclose failure behavior, such as unsupported URLs or restricted-content issues, but for this simple read tool the annotations cover the main risk 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 description is two tight sentences: it delivers the primary action first, then the main-use context, then the output shape. Every sentence earns its place and there is no redundancy or filler.
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 single-required-parameter, read-only tool with no nested objects, the description explains what it does, when to use it, and what kind of output is expected even though no output schema is declared. It would be richer if it handled failure cases or non-HTML URLs, but nothing critical is missing for an agent to invoke 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%, and both `url` and `userId` are already described in the schema. The tool description stays safe, so no additional parameter semantics is needed; at the same time, it provides no later parameter meaning beyond what the schema already states.
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 verb and resource: 'Extract clean text content from any URL.' It also names a concrete scenario (user-shared links like competitor sites, news articles, briefs, trend reports), which makes the tool's purpose unambiguous and distinguishes it from the surrounding graph-query siblings.
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 gives an explicit trigger: 'Use this when a user shares a link...' and gives representative examples. It does not name alternatives or exclusions, but none of the listed siblings overlap with URL content extraction, so the guidance is practical and sufficient.
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.
The tools are mostly distinct: search_graph, search_insights, search_statistics, and get_supplemental_context are carefully differentiated by output type, and graph exploration/evidence tools have clear roles. A couple of retrieval tools (get_validated_trends vs search_graph, search_statistics vs get_supplemental_context) could be mistaken at a glance, though their descriptions do enough to separate them.
All tool names use a consistent lowercase snake_case verb_noun pattern (search_*, get_*, list_*, generate_*, check_*, read_*). Verbs map predictably to actions, and there are no mixed conventions or vague generic names.
Fifteen tools is at the upper edge of the ideal range but each one maps to a distinct research workflow step: discovery, graph search, targeted retrieval, evidence, supplemental data, visualization, and account/capability checks. The breadth is justified by the server's broad trend-research scope.
The surface covers the full read-only research lifecycle: list graphs, search across them, drill into nodes/neighbors/evidence, get quantitative and qualitative answers, supplement thin coverage with external data, and produce visuals. Meta tools for account/capability and URL import prevent dead ends.