Epovest

Search the Atlas of sources

list_sources

Read-onlyIdempotent

The Atlas: the sources the AIs cite when they answer YOUR trackers. Each entry is a domain with its AI Authority on each AI, a 0 to 100 scale over the last 30 days where 100 is the source that AI cites the most. An engine with no value has not cited the domain lately, which is not a zero. Each AI has its own leader, so its own scale: each column ranks the sources on its own AI, the rankings being almost disjoint. The map carries the sources surfaced by the surveys of this account, so it grows as the account measures more; filter it to one brand with project_id. The scale, on the other hand, is computed across every measurement Epovest runs, which is what makes it stable. Use it to see where an answer comes from on a subject, and which places are worth existing on. The unit is the registrable domain, so a subdomain is folded into it and a hosting platform counts as one source, not one per author.

Input Schema

TableJSON Schema

Name	Required	Description
`page`	No	Page number, from 1.
`sort`	No	Order of the page: "aa_chatgpt", "aa_claude", "aa_gemini", "aa_perplexity", "aa_mistral" or "aa_grok" for the ranking of one AI, which also keeps only the sources that AI cites; "detections" ranks across all AIs at once, "engines" by number of distinct engines, "freshness" by most recent detection, "domain" alphabetically. Defaults to "detections".
`domain`	No	Keep only the domains containing this text, e.g. "wikipedia" or ".fr".
`engine`	No	Keep only the sources this engine has cited at least once.
`per_page`	No	Entries per page, 25 by default, 100 at most.
`project_id`	No	Only the sources surfaced by the trackers of this project: the UUID of a project of the account (call list_projects), or "default" for the trackers without a project. Use it to read the map of one brand when the account holds several. Omitted, the map covers every tracker of the account, which is what shows a source weighing on two of its markets at once. The AI Authority scale does not move with it: it stays computed across the whole map, so the figures of two projects compare.

TDQS

A4.5/5.0

Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true and idempotentHint=true, but the description adds substantial behavioral context beyond that: the 30-day scale, the distinction that 'no value is not a zero', domain folding, and the fact that the scale is stable across measurements and does not change with project_id. This informs the agent about data semantics and non-obvious behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but densely packed with necessary context about the data model. It is front-loaded with the core purpose and then explains the scale, unit, and filtering nuances. No wasted sentences, though it could be slightly more structured with breaks, it remains readable and efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Without an output schema, the description adequately explains what each entry contains (domain, AI Authority per AI, scale) and how filtering works. It lacks an explicit statement of the response format (e.g., a list of objects), but the prose strongly implies it. Given the complexity, this is nearly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. However, the description adds meaningful nuance for project_id (explains that the scale does not move with it, enabling cross-project comparisons) and clarifies the meaning of the AI Authority scale referenced by the sort parameter. This goes beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists sources from the Atlas, each with AI Authority per AI. It distinguishes from siblings by explaining the data scope (the sources AIs cite) and the unit (registrable domain). The opening sentence is specific and not a tautology.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a clear use case: 'Use it to see where an answer comes from on a subject, and which places are worth existing on.' It does not explicitly name alternatives or exclusions, but the context is unambiguous; the detailed filtering options imply when to use it. No misleading guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4/5.0

Disambiguation4/5

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.

Naming Consistency5/5

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.

Tool Count1/5

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.

Completeness5/5

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.

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