get_sourceOne entry of the Atlas, read by its id: the domain and its AI Authority on each AI. Call list_sources to find a source id, or to read the same entries filtered and ranked.
| Name | Required | Description | Default |
|---|---|---|---|
| source_id | Yes | The UUID of the source: call list_sources to find it. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive behavior, so the description need not repeat that. It adds context beyond the annotations by explaining the result content ('domain and AI Authority') and implying this is an unfiltered/unranked fetch. No contradiction with 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?
Two tightly written sentences with zero redundancy. The core purpose is front-loaded, and the routing to list_sources comes second as a helpful supplement. Every word 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 single-parameter read tool with annotations covering safety and idempotency, the description is complete: it states what is returned, how to obtain the id, and when to use the sibling tool. No output schema exists, but the description gives sufficient expectation of the result. No missing critical context.
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 already covers the sole parameter (source_id) with format and guidance to call list_sources. The tool description repeats this guidance without adding new semantic detail. With 100% schema coverage, the baseline of 3 is appropriate; the description does not degrade or enhance the parameter understanding.
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 ('read'), a specific resource ('one entry of the Atlas'), and the exact return content ('the domain and its AI Authority on each AI'). It also distinguishes itself from list_sources by noting the filtering/ranking alternative, making the tool's identity clear.
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?
Explicitly instructs the agent to call list_sources to find a source id or to read filtered/ranked entries, thereby drawing a clear line between this tool and its sibling. The agent knows exactly when to use this tool (single entry by id) and when to use the alternative.
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.
Each tool maps to a distinct resource and action, and the descriptions go out of their way to separate near-neighbor concepts like surfaces vs corroborations and score series vs raw responses. A few related pairs (get_results/get_responses, get_credits/get_usage, create_surface/create_corroboration) could still be confused at a glance, so it is not a perfect 5.
Tool names follow a highly consistent verb_noun snake_case pattern across all 67 tools, with clear families like create_, update_, get_, list_, archive_, restore_, and delete_. Minor quirks such as topup_credits as one word do not break the overall uniformity.
67 tools is an extreme count for a single MCP server, even for a broad brand-monitoring domain. The surface is bloated with lifecycle variants per entity, and the sheer number makes the server hard to navigate and prompt against.
The server covers full lifecycles for projects, trackers, surfaces, corroborations, quests, logbook entries, keyword discoveries, competitor scans, link targets, sources, support, and billing. Archive/restore and soft-delete paths prevent dead ends, and nearly every obvious workflow has a corresponding tool.