Return the most recent citation score for the authenticated client,
along with the measurements from that same week (safe fields only).
Raw data — no healthy/critical classification applied.
Use when an agent needs to audit how generative engines cite a client:
citation rate, average position, sentiment, competitors mentioned and
entity fidelity.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint annotation, the description discloses that data is raw without classification, returns 'safe fields only', and is scoped to the authenticated client. It also specifies the exact measurement types (citation rate, position, sentiment, etc.), which is rich behavioral context beyond the basic annotation.
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 sentences, front-loaded with the core action and result, followed by the raw-data caveat and usage guidance. Each sentence provides distinct value: what it returns, how it's presented, and when to use it.
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 no output schema, the description covers the essential return content (citation score + week measurements), the filtering ('safe fields only'), the lack of classification, and the intended use case. It is complete for an agent to decide when to invoke this tool and what to expect.
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?
The tool has zero parameters, so the baseline is 4. The description clarifies that the client is implicit ('for the authenticated client'), which adds meaning about how the tool identifies its subject, despite having no schema parameters to document.
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 uses the specific verb 'Return' with a clear resource ('most recent citation score') and adds scope details ('measurements from that same week', 'safe fields only', 'Raw data — no healthy/critical classification applied'). This clearly distinguishes it from siblings that might provide classification or different metrics.
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 explicitly states when to use: 'Use when an agent needs to audit how generative engines cite a client' and lists the specific metrics. It implies when not to use (when a classification is needed) via 'Raw data — no healthy/critical classification applied', but does not name an alternative 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.
Each tool targets a distinct resource and action, though analyze_geo overlaps slightly with get_bleed_model and get_competitor_actions. Descriptions clarify the differences, making misselection unlikely.
All tools follow a consistent snake_case verb_noun pattern (activate_artifact, get_bot_activity, mark_applied). Minor deviations like analyze_geo instead of get_geo_analysis don't break the pattern.
16 tools is slightly above the typical 3-15 range, but the server covers multiple subdomains (artifacts, analysis, quick wins, reporting), so each tool has a clear purpose.
The tool set covers the full artifact lifecycle (generate, list, get, approve, activate, regenerate) and the core analysis metrics. Minor gaps like no explicit delete tool for artifacts exist, but regenerate serves that need.